CN111507923B - Noise processing method, device, equipment and medium for video image - Google Patents
Noise processing method, device, equipment and medium for video image Download PDFInfo
- Publication number
- CN111507923B CN111507923B CN202010318649.3A CN202010318649A CN111507923B CN 111507923 B CN111507923 B CN 111507923B CN 202010318649 A CN202010318649 A CN 202010318649A CN 111507923 B CN111507923 B CN 111507923B
- Authority
- CN
- China
- Prior art keywords
- value
- noise reduction
- noise
- image
- pixel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 14
- 230000009467 reduction Effects 0.000 claims abstract description 354
- 238000012545 processing Methods 0.000 claims abstract description 77
- 238000000034 method Methods 0.000 claims abstract description 51
- 238000003860 storage Methods 0.000 claims abstract description 9
- 238000001914 filtration Methods 0.000 claims description 66
- 238000004590 computer program Methods 0.000 claims description 14
- 230000008569 process Effects 0.000 abstract description 23
- 238000010586 diagram Methods 0.000 description 22
- 238000004891 communication Methods 0.000 description 9
- 230000008859 change Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000005070 sampling Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
- G06T7/248—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments involving reference images or patches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a noise processing method, device, equipment and medium for video images, which are used for solving the problem of excessive storage resources consumed in the existing noise processing process of video images. When the noise value of each first noise reduction area of the current frame image is determined, the target noise value of the first noise reduction area can be determined through the target average brightness value of the first noise reduction area and the corresponding relation between the pre-stored average brightness value and the noise value.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a method, an apparatus, a device, and a medium for processing noise of a video image.
Background
The signal-to-noise ratio is one of the most basic video signal quality indicators. Various noises are inevitably introduced in the processes of video image acquisition, processing and transmission, and noise reduction processing is required to be carried out on the video image in order to improve the signal-to-noise ratio of the video image. In the prior art, when noise reduction processing is performed on a video image, a noise value of the video image is generally required to be determined, noise reduction intensity of the video image is determined according to the noise value, and noise reduction processing is performed on the video image, so that noise residues caused by smear or insufficient noise reduction intensity due to excessive noise reduction intensity are avoided.
In the prior art, when the current frame image is subjected to noise reduction processing, the current frame image is divided into a plurality of noise reduction areas, noise reduction intensity of each noise reduction area is stored, the noise reduction intensity of each noise reduction area is determined by a difference value between a pixel value of each noise reduction area of the image after noise reduction of the previous frame image and a pixel value of each noise reduction area corresponding to the image before noise reduction, and noise reduction processing is performed based on the stored noise reduction intensity of each noise reduction area. FIG. 1 is a flowchart of a method for determining noise reduction intensity of each noise reduction region of a current frame image in the prior art, the process includes:
S101: and respectively determining the difference value of the pixel value of each noise reduction region of the current frame image before noise reduction processing and the pixel value of each noise reduction region of the current frame image after noise reduction processing, and taking the difference value as the noise value of each noise reduction region of the current frame image.
S102: and determining the average value of the noise values of the current frame image according to the noise value of each noise reduction area of the current frame image.
S103: and comparing the difference value of each noise reduction region with the average value of the noise values respectively, and determining the noise reduction intensity of each noise reduction region of the current frame image.
Fig. 2 is a schematic diagram of a process of denoising an i-th frame image of a video in the prior art, where the process includes:
specifically, the ith frame image is uniformly divided into N noise reduction areas, three-dimensional noise reduction is carried out by respectively using the noise reduction intensity of each noise reduction area of the stored ith-1 frame image aiming at each noise reduction area, and the average value of the difference values of the pixel values of each noise reduction area of the ith frame image before three-dimensional noise reduction and after three-dimensional noise reduction is determined as a noise value; for the noise value of each noise reduction area, determining the average value of the noise value of the ith frame of image by a histogram statistical method; the horizontal axis of the histogram is the difference value, the vertical axis is the number of noise reduction areas, the noise reduction intensity of each noise reduction area corresponding to the ith frame image is determined according to the difference value of the noise value of each noise reduction area and the average value of the noise values, the noise reduction intensity of each noise reduction area corresponding to the ith+1st frame image is used as the noise reduction intensity of each noise reduction area corresponding to the ith+1st frame image, three-dimensional noise reduction is carried out on the ith+1st frame image, and then noise reduction processing of the ith+1st frame image is carried out according to the flow.
In the prior art, when the noise reduction processing is performed on the current frame image, the pixel value of the pixel point is input in a row unit during the input of the current frame image, and when the noise reduction intensity corresponding to each noise reduction area of the current frame image is stored, the pixel value of the pixel point of each row of the current frame image after the noise reduction processing needs to be determined.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a medium for processing noise of a video image, which are used for solving the problem of excessive storage resources consumed in the existing noise processing process of the video image.
The embodiment of the invention provides a noise processing method of a video image, which comprises the following steps:
determining a target average brightness value of each first noise reduction area of the current frame image;
Determining a target noise value of each first noise reduction region of the current frame image according to the target average brightness value of each first noise reduction region and the corresponding relation between the pre-stored average brightness value and the noise value;
and carrying out noise reduction processing on each first noise reduction region of the current frame image according to the target noise value of each first noise reduction region of the current frame image.
Further, the determining the target average brightness value of each first noise reduction area of the current frame image includes:
for each first noise reduction region, determining a target noise reduction range containing the first noise reduction region according to a preset region range, and determining a target average brightness value of the first noise reduction region according to the brightness value of each pixel point contained in the target noise reduction range.
Further, the method comprises:
determining a first difference image according to the pixel value of each pixel point of the current frame image and the difference value of the pixel value of the corresponding pixel point of the previous frame image of the current frame image;
taking absolute values of pixel values of each pixel point in the first difference image to determine a corresponding second difference image;
taking the absolute value of the pixel value of each pixel point in the image subjected to the filtering processing of the first difference image to obtain a first filtering image; filtering the second difference image to obtain a second filtered image; taking the pixel value difference value of each corresponding pixel point in the second filter image and the first filter image as the noise value of each pixel point;
And determining a target corresponding relation between the brightness value and the noise value according to the brightness value of each pixel point in the current frame image and the noise value of each pixel point, and updating the stored corresponding relation by adopting the target corresponding relation.
Further, the step of taking the pixel value difference value of each corresponding pixel point in the second filtered image and the first filtered image as the noise value of each pixel point includes:
dividing the first filtering image and the second filtering image into a plurality of second noise reduction areas according to the same dividing mode;
and determining a pixel value difference value of each corresponding pixel point in the second noise reduction region aiming at each corresponding second noise reduction region, and taking an average value of the pixel value difference values of each pixel point as a noise value of each pixel point in the second noise reduction region.
Further, the determining the target correspondence between the luminance value and the noise value according to the luminance value of each pixel point in the current frame image and the noise value of each pixel point includes:
dividing the current frame image into a plurality of second noise reduction areas according to the same dividing mode as the filtering image;
for each second noise reduction region, determining an average brightness value of the second noise reduction region according to the brightness value of each pixel point in the second noise reduction region;
And determining the target corresponding relation between the brightness value and the noise value according to the average value of the average brightness value and the pixel value difference value of each second noise reduction area.
Further, the target correspondence between the luminance value and the noise value satisfies a linear relationship.
The embodiment of the invention provides a noise processing device for video images, which comprises:
the determining module is used for determining a target average brightness value of each first noise reduction area of the current frame image; determining a target noise value of each first noise reduction region of the current frame image according to the target average brightness value of each first noise reduction region and the corresponding relation between the pre-stored average brightness value and the noise value;
the noise reduction module is used for carrying out noise reduction processing on each first noise reduction area of the current frame image according to the target noise value of each first noise reduction area of the current frame image.
Further, the determining module is specifically configured to determine, for each first noise reduction region, a target noise reduction range including the first noise reduction region according to a preset region range, and determine, according to a luminance value of each pixel point included in the target noise reduction range, a target average luminance value of the first noise reduction region.
Further, the determining module is further configured to determine a first difference image according to a difference between a pixel value of each pixel point of the current frame image and a pixel value of a corresponding pixel point of a previous frame image of the current frame image; taking absolute values of pixel values of each pixel point in the first difference image to determine a corresponding second difference image;
the apparatus further comprises:
the filtering module is used for obtaining a first filtering image by taking the absolute value of the pixel value of each pixel point in the image subjected to the filtering processing of the first difference image; filtering the second difference image to obtain a second filtered image; taking the pixel value difference value of each corresponding pixel point in the second filter image and the first filter image as the noise value of each pixel point;
and the updating module is used for determining a target corresponding relation between the brightness value and the noise value according to the brightness value of each pixel point in the current frame image and the noise value of each pixel point, and updating the stored corresponding relation by adopting the target corresponding relation.
Further, the filtering module is specifically configured to divide the first filtered image and the second filtered image into a plurality of second noise reduction areas according to the same division manner; and determining a pixel value difference value of each corresponding pixel point in the second noise reduction region aiming at each corresponding second noise reduction region, and taking an average value of the pixel value difference values of each pixel point as a noise value of each pixel point in the second noise reduction region.
Further, the updating module is specifically configured to divide the current frame image into a plurality of second noise reduction areas according to the same division manner as the filtered image; for each second noise reduction region, determining an average brightness value of the second noise reduction region according to the brightness value of each pixel point in the second noise reduction region; and determining the target corresponding relation between the brightness value and the noise value according to the average value of the average brightness value and the pixel value difference value of each second noise reduction area.
An embodiment of the present invention provides an electronic device, where the electronic device includes a processor and a memory, where the memory is configured to store program instructions, and where the processor is configured to implement steps of a method for noise processing a video image according to any one of the methods described above when executing a computer program stored in the memory.
An embodiment of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the noise processing method of any one of the video images described above.
The embodiment of the invention provides a noise processing method, a device, equipment and a medium for a video image, wherein when the noise value of each first noise reduction area of a current frame image is determined, the target noise value of the first noise reduction area can be determined through the target average brightness value of the first noise reduction area and the corresponding relation between the average brightness value and the noise value stored in advance.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it will be apparent that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of determining noise reduction intensity for each noise reduction region of a current frame image in the prior art;
FIG. 2 is a schematic diagram of a prior art process for denoising an i-th frame image of a video;
fig. 3 is a schematic process diagram of a method for processing noise of a video image according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a method for determining a target noise reduction range according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a process for updating a correspondence between a stored average luminance value and a noise value according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a target correspondence relationship between a noise value and a luminance value according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of another embodiment of a noise processing procedure of a video image;
Fig. 8 is a schematic structural diagram of a device for processing noise of a video image according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to save storage resources in video image noise processing, the embodiment of the invention provides a video image noise processing method, device, equipment and medium.
Example 1:
fig. 3 is a schematic process diagram of a method for processing noise of a video image according to an embodiment of the present invention, where the process includes the following steps:
s301: a target average luminance value for each first noise reduction region of the current frame image is determined.
The noise processing method of the video image is applied to the electronic equipment for noise processing, and the electronic equipment can be an image acquisition equipment, such as a network camera, a smart ball machine and the like.
For a current frame image of a video subjected to noise processing, the current frame image comprises at least more than two first noise reduction areas, wherein each first noise reduction area comprises a set number of pixel points. And the number of the pixel points contained in each first noise reduction area can be the same or different. Preferably, the number of pixels included in each first noise reduction area is the same, and the set number may be, for example, 1, 4, etc. In order to accurately perform noise processing on the current frame image, preferably, each first noise reduction area includes a pixel point.
In order to realize noise processing on a video image, determining a target average brightness value of each first noise reduction area of a current frame image subjected to noise processing; if the first noise reduction region comprises a pixel point, the target average brightness value of the first noise reduction region is specifically the brightness value of the pixel point in the first noise reduction region; if the first noise reduction region includes at least two pixels, the target average luminance value of the first noise reduction region is specifically an average value of the sum of luminance values of all pixels in the first noise reduction region.
S302: and determining the target noise value of each first noise reduction region of the current frame image according to the target average brightness value of each first noise reduction region and the corresponding relation between the pre-stored average brightness value and the noise value.
The electronic equipment for noise processing pre-stores the corresponding relation between the average brightness value and the noise value, and determines the target noise value corresponding to each target average brightness value according to the pre-stored corresponding relation between the average brightness value and the noise value after determining the target average brightness value of each first noise reduction area for each first noise reduction area of the current frame image.
S303: and carrying out noise reduction processing on each first noise reduction region of the current frame image according to the target noise value of each first noise reduction region of the current frame image.
After the target noise value of each first noise reduction area of the current frame image is determined, noise reduction processing is carried out on each corresponding first noise reduction area of the current frame image according to the size of the target noise value of each first noise reduction area. Specifically, when the noise reduction processing is performed on the first noise reduction area, a method in the prior art may be adopted, which is not described in detail in the embodiment of the present invention.
In the embodiment of the invention, when the noise value of each first noise reduction area of the current frame image is determined, the target noise value of the first noise reduction area can be determined through the target average brightness value of the first noise reduction area and the corresponding relation between the average brightness value and the noise value which are stored in advance.
Example 2:
in order to determine the luminance value of the noise reduction region more accurately, in the embodiment of the present invention, the determining the target average luminance value of each first noise reduction region of the current frame image includes:
for each first noise reduction region, determining a target noise reduction range containing the first noise reduction region according to a preset region range, and determining a target average brightness value of the first noise reduction region according to the brightness value of each pixel point contained in the target noise reduction range.
In order to accurately determine the target average brightness value of each first noise reduction region of the current frame image, a region range is preset; the size of the area range can be flexibly set according to the requirement, and the area range can be a circular area range or a rectangular area range.
For each first noise reduction region, according to a preset region range, a region range including the first noise reduction region can be determined, and according to a region range including the first noise reduction region, a target noise reduction range including the first noise reduction region can be determined.
When the target noise reduction range including the first noise reduction region is determined, the first noise reduction region may be in the center of the target noise reduction range, or other preset positions, such as the upper left corner, the lower right corner, etc., and the target noise reduction range including the first noise reduction region may be determined according to the first noise reduction region and the preset region range. When the target noise reduction range is determined, if a part of a certain first noise reduction region is located in the preset region range, the part of the first noise reduction region can be considered to be located in the first noise reduction region of the region range, and all the first noise reduction regions are contained in the determined target noise reduction range. Or when part of a certain first noise reduction area is positioned in the preset area range and the area of the part is larger than the preset area threshold, all the first noise reduction area is included in the determined target noise reduction range.
Fig. 4 is a schematic diagram of a method for determining a target noise reduction range according to an embodiment of the present invention, specifically, as shown in fig. 4, when determining a target noise reduction range of a first noise reduction region 9, taking a preset region range as an example of a circular region range, where the circular region range includes a part of regions of the first noise reduction regions 1, 2, 3, 4, 5, 6, 7, 8 and all regions of the first noise reduction region 9, and determining all regions of the first noise reduction regions 1, 2, 3, 4, 5, 6, 7, 8, 9 as the target noise reduction range including the first noise reduction region 9.
After determining the target noise reduction range including the first noise reduction region, determining an average value of brightness values of all pixel points in the target noise reduction range, and determining the average value as a target average brightness value of the first noise reduction region.
The above figures illustrate an example, in which the target noise reduction range including the first noise reduction region 9 is a region range formed by all the regions of the first noise reduction regions 1, 2, 3, 4, 5, 6, 7, 8, and 9 together, and an average value of the sum of the luminance values of each pixel point in the target noise reduction range is determined, and the average value is the target average luminance value of the first noise reduction region 9.
Example 3:
in order to update the saved correspondence, based on the above embodiments, in the embodiments of the present invention, the method includes:
determining a first difference image according to the pixel value of each pixel point of the current frame image and the difference value of the pixel value of the corresponding pixel point of the image after the noise reduction treatment is completed in the previous frame of the current frame image;
taking absolute values of pixel values of each pixel point in the first difference image to determine a corresponding second difference image;
taking the absolute value of the pixel value of each pixel point in the image subjected to the filtering processing of the first difference image to obtain a first filtering image; filtering the second difference image to obtain a second filtered image; taking the pixel value difference value of each corresponding pixel point in the second filter image and the first filter image as the noise value of each pixel point;
and determining a target corresponding relation between the brightness value and the noise value according to the brightness value of each pixel point in the current frame image and the noise value of each pixel point, and updating the stored corresponding relation by adopting the target corresponding relation.
When the noise processing is performed on the video image, in order to realize accurate noise processing on each frame of image, the stored corresponding relation needs to be updated according to the current frame of image.
Specifically, according to the current frame image and the previous frame image of the current frame image, determining the difference value between the pixel value of each pixel point of the current frame image and the pixel value of the corresponding pixel point of the previous frame image, wherein the previous frame image refers to the previous frame image after the noise reduction treatment is completed; according to the corresponding difference value of each pixel point, a first difference image can be determined, wherein the pixel value of each pixel point of the first difference image can be positive or negative.
Because the pixel value of each pixel point in the first difference image may be a positive value or a negative value, in order to obtain a second difference image with the pixel value being a positive value, the pixel value of each pixel point in the first difference image is taken as an absolute value, so as to obtain the second difference image.
And respectively carrying out filtering processing on the first difference image and the second difference image, and specifically, adopting the same filtering processing method to carry out filtering processing on the first difference image and the second difference image. The filtering processing method comprises the following steps: mean filtering, median filtering, and other filtering processes, etc. After the filtering process is completed, the pixel value of each pixel point in the first difference image may be a positive value or a negative value, so that the absolute value of the pixel value of each pixel point after the filtering process is completed on the first difference image is taken to obtain a first filtered image for convenience of subsequent comparison. And determining the image of the second difference image subjected to the filtering processing as a second filtered image.
And determining the pixel value difference value of each pixel point of the second filter image and the pixel value difference value of the corresponding pixel point of the first filter image as the noise value of each pixel point of the current frame image.
This is because the first difference image contains not only the pixel value difference value due to noise but also the pixel value difference value due to scene change, and because noise has randomness, it appears that the pixel value difference value is positive or negative, and filtering the first difference image causes the pixel value difference value due to noise to approach 0, so that after filtering the first difference image, the pixel value difference value due to noise is eliminated, and the pixel value of each pixel point after filtering the first difference image takes an absolute value to obtain a first filtered image, where the first filtered image contains only the pixel value difference value due to scene change.
Since the pixel value of the pixel point in the second difference image is the absolute value of the pixel value of each pixel point of the first difference image, the second filtered image after the second difference image is subjected to the filtering processing contains the pixel value difference value caused by noise and the pixel value difference value caused by scene change. And taking the pixel value difference value of each corresponding pixel point in the second filter image and the first filter image as the noise value of each pixel point, so that the influence of scene change on the pixel value can be eliminated, and the determined noise value of each pixel point is more accurate.
Specifically, in order to ensure accuracy of determining the noise value of the pixel point, in an embodiment of the present invention, the step of taking the difference value of the pixel value of each corresponding pixel point in the second filtered image and the first filtered image as the noise value of each pixel point includes:
dividing the first filtering image and the second filtering image into a plurality of second noise reduction areas according to the same dividing mode;
and determining a pixel value difference value of each corresponding pixel point in the second noise reduction region aiming at each corresponding second noise reduction region, and taking an average value of the pixel value difference values of each pixel point as a noise value of each pixel point in the second noise reduction region.
In order to determine the noise value of each pixel point, in the embodiment of the present invention, the first filtered image and the second filtered image are divided into a plurality of second noise reduction areas according to the same division manner, so as to determine the noise value of each pixel point in each second noise reduction area.
In order to determine the noise value of each pixel, the difference between the corresponding pixels in the first filtered image and the second filtered image may be determined as the noise value of the pixel. However, in order to ensure the accuracy of determining the noise value of the pixel point, in the embodiment of the present invention, the first filtered image and the second filtered image are divided into a plurality of second noise reduction areas by adopting the same division manner, where the size of each noise reduction area may be the same or different, and the number of the pixel points included in each noise reduction area is at least one.
And determining a pixel value difference value of each corresponding pixel point in the second noise reduction region of the second filtered image and the first filtered image aiming at each second noise reduction region, and taking an average value of the pixel value difference values of each pixel point in the second noise reduction region as a noise value of the second noise reduction region.
After determining the noise value of each second noise reduction area, because the embodiment of the invention determines the target noise value of the first noise reduction area according to the saved corresponding relation between the brightness value and the noise value, in order to facilitate the noise reduction processing on the next frame image, in the embodiment of the invention, the target corresponding relation between the brightness value and the noise value is determined according to the brightness value of each pixel point in the current frame image and the noise value of each pixel point, so that the corresponding relation between the saved brightness value and the noise value is updated by adopting the target corresponding relation.
In order to ensure the accuracy of the noise reduction processing for the next frame image, in the embodiment of the present invention, determining, according to the luminance value of each pixel point in the current frame image and the noise value of each pixel point, the target correspondence between the luminance value and the noise value includes:
Dividing the current frame image into a plurality of second noise reduction areas according to the same dividing mode as the filtering image;
for each second noise reduction region, determining an average brightness value of the second noise reduction region according to the brightness value of each pixel point in the second noise reduction region;
and determining the target corresponding relation between the brightness value and the noise value according to the average value of the average brightness value and the pixel value difference value of each second noise reduction area.
In order to ensure the accuracy of the noise reduction processing of the next frame image, the stored correspondence may be updated, so that after the noise value of each second noise reduction area is determined, the brightness value of each second noise reduction area needs to be determined. Specifically, the current frame image is divided into a plurality of second noise reduction areas according to the same division mode as the filtered image; for each second noise reduction region, an average value of the luminance values of each pixel point in the second noise reduction region is taken as an average luminance value of the second noise reduction region.
And determining a target corresponding relation between the brightness value and the noise value of the current frame image according to the average value and the average brightness value of the pixel value difference value of each second noise reduction area of the current frame image, and updating the stored corresponding relation according to the target corresponding relation.
Fig. 5 is a schematic diagram of a process for updating a correspondence between a stored average brightness value and a noise value according to an embodiment of the present invention, where the process includes the following steps:
s501: dividing the current frame image into M x N second noise reduction areas; for each second noise reduction region, determining the average brightness value of the second noise reduction region according to the brightness value of each pixel point in the second noise reduction region. And determining a first difference image according to the pixel value of each pixel point of the current frame image and the difference value of the pixel value of the corresponding pixel point of the image of which the previous frame of the current frame image is subjected to the noise reduction treatment. S502 and S503 are entered respectively.
S502: and determining a second difference image corresponding to the absolute value of the pixel value of each pixel point in the first difference image, and performing m×n mean value filtering processing on the second difference image to obtain a second filtered image. The process advances to S504.
S503: and performing m-by-n average filtering processing on the first difference image, and taking the absolute value of the pixel point of the first difference image after the filtering processing to obtain a first filtering image. The process advances to S504.
S504: dividing the first filtered image and the second filtered image into M x N second noise reduction areas; and determining a pixel value difference value of each corresponding pixel point in each corresponding second noise reduction region, and taking an average value of the pixel value difference values of each pixel point as a noise value of the second noise reduction region.
S505: and determining the target corresponding relation between the average brightness value and the noise value according to the average brightness value and the noise value of each second noise reduction area.
S506: and updating the corresponding relation between the stored average brightness value and the noise value according to the target corresponding relation.
In the embodiment of the invention, when the noise reduction processing is performed on the current frame image, the corresponding relation between the stored brightness value and the noise value is updated on the current frame image, so that the target noise value of each noise reduction area of each frame image determined according to the brightness value before the noise processing is performed on each frame image can be ensured to be accurate. The brightness value is not suddenly changed, so that the target noise value of each noise reduction area is not suddenly changed, the situation that a pseudo boundary appears due to different noise reduction intensities among noise reduction areas caused by the suddenly changed noise value adopted by adjacent noise reduction areas in a wide dynamic scene with uneven noise distribution is avoided, and therefore, the noise processing method provided by the embodiment of the invention can be suitable for the wide dynamic scene with uneven noise distribution, and the scene applicability of the noise processing method is improved.
Example 4:
in order to accurately determine the target correspondence between the noise value and the luminance value, in the embodiments of the present invention, the target correspondence between the luminance value and the noise value satisfies a linear relationship.
According to the determined noise value and brightness value of each second noise reduction area of the current frame image, a random sampling coincidence algorithm in the prior art is adopted, so that the corresponding relation of the target can be determined, and the corresponding relation of the target meets the linear relation.
Specifically, the random sample consensus algorithm is to fit a straight line based on a set of planar points. Fig. 6 is a schematic diagram of a target correspondence between a noise value and a luminance value, as shown in fig. 6, for each second noise reduction region, the noise value and the average luminance value of each second noise reduction region form a coordinate point, where the average luminance value is an abscissa, the noise value is an ordinate, the noise values and the average luminance value of all the second noise reduction regions form a first point set (xi, yi), where xi is the average luminance value of the ith second noise reduction region, yi is the noise value of the ith second noise reduction region, and the random sampling coincidence algorithm is briefly described as follows:
1. randomly selecting 2 coordinate points from the first point set (xi, yi) to obtain a straight line y=kx+b;
2. calculating the distance from other coordinate points to the straight line, and determining a second point set with the distance smaller than the first threshold value;
3. if the number of coordinate points in the second point set is greater than a second threshold value, the straight line y=kx+b is the straight line corresponding to the second point set;
4. If the number of the coordinate points in the second point set is smaller than a second threshold value, selecting new 2 coordinate points, and repeating the process;
after repeating N times, a second point set with the most coordinate points can be determined, and a straight line corresponding to the second point set with the most coordinate points is the corresponding relation of the targets.
Example 5:
the noise processing procedure of the video image according to the present invention will be described with reference to a specific embodiment, and fig. 7 is a schematic diagram of another noise processing procedure of the video image according to an embodiment of the present invention, where the procedure includes the following steps:
s701: and constructing a k window by taking a first noise reduction area to be processed of the current frame image as a center, calculating the average value of brightness values of all pixel points in the window, and taking the average value as a target brightness value of the first noise reduction area to be processed. Wherein each first noise reduction region comprises a pixel point.
S702: and inquiring the corresponding relation between the stored brightness value and the noise value determined according to the previous frame image, and determining a target noise value corresponding to the first noise reduction area to be processed of the current frame input image.
S703: and carrying out noise reduction processing on the first noise reduction area to be processed according to the target noise value.
S704: dividing the current frame image before noise reduction into M.N second noise reduction areas; for each second noise reduction region, determining the average brightness value of the second noise reduction region according to the brightness value of each pixel point in the second noise reduction region. And determining a first difference image according to the pixel value of each pixel point of the current frame image before the noise reduction processing and the difference value of the pixel value of the corresponding pixel point of the image of which the noise reduction processing is completed in the previous frame of the current frame image. S705 and S706 are entered.
S705: and determining a second difference image corresponding to the absolute value of the pixel value of each pixel point in the first difference image, and performing m×n mean value filtering processing on the second difference image to obtain a second filtered image. The process advances to S707.
S706: and performing m-by-n average filtering processing on the first difference image, and taking the absolute value of the pixel point of the first difference image after the filtering processing to obtain a first filtering image. The process advances to S707.
S707: dividing the first filtered image and the second filtered image into M x N second noise reduction areas; and determining a pixel value difference value of each corresponding pixel point in each corresponding second noise reduction region, and taking an average value of the pixel value difference values of each pixel point as a noise value of the second noise reduction region. S708: and determining the target corresponding relation between the average brightness value and the noise value by adopting a random sampling coincidence algorithm according to the average brightness value and the noise value of each second noise reduction area.
S709: and updating the corresponding relation between the stored average brightness value and the noise value according to the target corresponding relation.
Example 6:
fig. 8 is a schematic structural diagram of a video image noise processing apparatus according to an embodiment of the present invention, where on the basis of the foregoing embodiments, the embodiment of the present invention provides a video image noise processing apparatus, the apparatus includes:
a determining module 801, configured to determine a target average luminance value of each first noise reduction area of the current frame image; determining a target noise value of each first noise reduction region of the current frame image according to the target average brightness value of each first noise reduction region and the corresponding relation between the pre-stored average brightness value and the noise value;
the noise reduction module 802 is configured to perform noise reduction processing on each first noise reduction area of the current frame image according to a target noise value of each first noise reduction area of the current frame image.
Further, the determining module 801 is specifically configured to determine, for each first noise reduction region, a target noise reduction range including the first noise reduction region according to a preset region range, and determine, according to a luminance value of each pixel point included in the target noise reduction range, a target average luminance value of the first noise reduction region.
Further, the determining module 801 is further configured to determine a first difference image according to a difference between a pixel value of each pixel of the current frame image and a pixel value of a corresponding pixel of a previous frame image of the current frame image; taking absolute values of pixel values of each pixel point in the first difference image to determine a corresponding second difference image;
the apparatus further comprises:
the filtering module 803 is configured to take an absolute value of a pixel value of each pixel point in the image after the filtering process is performed on the first difference image to obtain a first filtered image; filtering the second difference image to obtain a second filtered image; taking the pixel value difference value of each corresponding pixel point in the second filter image and the first filter image as the noise value of each pixel point;
and an updating module 804, configured to determine a target correspondence between a luminance value and a noise value according to a luminance value of each pixel point in the current frame image and the noise value of each pixel point, and update the stored correspondence with the target correspondence.
Further, the filtering module 803 is specifically configured to divide the first filtered image and the second filtered image into a plurality of second noise reduction areas according to the same division manner; and determining a pixel value difference value of each corresponding pixel point in the second noise reduction region aiming at each corresponding second noise reduction region, and taking an average value of the pixel value difference values of each pixel point as a noise value of each pixel point in the second noise reduction region.
Further, the updating module 804 is specifically configured to divide the current frame image into a plurality of second noise reduction areas according to the same division manner as the filtered image; for each second noise reduction region, determining an average brightness value of the second noise reduction region according to the brightness value of each pixel point in the second noise reduction region; and determining the target corresponding relation between the brightness value and the noise value according to the average value of the average brightness value and the pixel value difference value of each second noise reduction area.
Example 7:
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and on the basis of the foregoing embodiments, the embodiment of the present invention further provides an electronic device, which includes a processor 901, a communication interface 902, a memory 903, and a communication bus 904, where the processor 901, the communication interface 902, and the memory 903 complete communication with each other through the communication bus 904;
the memory 903 has stored therein a computer program which, when executed by the processor 501, causes the processor 901 to perform the steps of:
determining a target average brightness value of each first noise reduction area of the current frame image;
determining a target noise value of each first noise reduction region of the current frame image according to the target average brightness value of each first noise reduction region and the corresponding relation between the pre-stored average brightness value and the noise value;
And carrying out noise reduction processing on each first noise reduction region of the current frame image according to the target noise value of each first noise reduction region of the current frame image.
Further, the determining the target average brightness value of each first noise reduction area of the current frame image includes:
for each first noise reduction region, determining a target noise reduction range containing the first noise reduction region according to a preset region range, and determining a target average brightness value of the first noise reduction region according to the brightness value of each pixel point contained in the target noise reduction range.
Further, the method comprises:
determining a first difference image according to the pixel value of each pixel point of the current frame image and the difference value of the pixel value of the corresponding pixel point of the previous frame image of the current frame image;
taking absolute values of pixel values of each pixel point in the first difference image to determine a corresponding second difference image;
taking the absolute value of the pixel value of each pixel point in the image subjected to the filtering processing of the first difference image to obtain a first filtering image; filtering the second difference image to obtain a second filtered image; taking the pixel value difference value of each corresponding pixel point in the second filter image and the first filter image as the noise value of each pixel point;
And determining a target corresponding relation between the brightness value and the noise value according to the brightness value of each pixel point in the current frame image and the noise value of each pixel point, and updating the stored corresponding relation by adopting the target corresponding relation.
Further, the step of taking the pixel value difference value of each corresponding pixel point in the second filtered image and the first filtered image as the noise value of each pixel point includes:
dividing the first filtering image and the second filtering image into a plurality of second noise reduction areas according to the same dividing mode;
and determining a pixel value difference value of each corresponding pixel point in the second noise reduction region aiming at each corresponding second noise reduction region, and taking an average value of the pixel value difference values of each pixel point as a noise value of each pixel point in the second noise reduction region.
Further, the determining the target correspondence between the luminance value and the noise value according to the luminance value of each pixel point in the current frame image and the noise value of each pixel point includes:
dividing the current frame image into a plurality of second noise reduction areas according to the same dividing mode as the filtering image;
for each second noise reduction region, determining an average brightness value of the second noise reduction region according to the brightness value of each pixel point in the second noise reduction region;
And determining the target corresponding relation between the brightness value and the noise value according to the average value of the average brightness value and the pixel value difference value of each second noise reduction area.
Further, the target correspondence between the luminance value and the noise value satisfies a linear relationship.
The communication bus mentioned above for the electronic devices may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface 902 is used for communication between the electronic device and other devices.
The Memory may include random access Memory (Random Access Memory, RAM) or may include Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit, a network processor (Network Processor, NP), etc.; but also digital instruction processors (Digital Signal Processing, DSP), application specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
Example 8:
on the basis of the above embodiments, the embodiments of the present invention also provide a computer-readable storage medium storing a computer program, the computer program being executed by a processor to:
determining a target average brightness value of each first noise reduction area of the current frame image;
determining a target noise value of each first noise reduction region of the current frame image according to the target average brightness value of each first noise reduction region and the corresponding relation between the pre-stored average brightness value and the noise value;
and carrying out noise reduction processing on each first noise reduction region of the current frame image according to the target noise value of each first noise reduction region of the current frame image.
Further, the determining the target average brightness value of each first noise reduction area of the current frame image includes:
for each first noise reduction region, determining a target noise reduction range containing the first noise reduction region according to a preset region range, and determining a target average brightness value of the first noise reduction region according to the brightness value of each pixel point contained in the target noise reduction range.
Further, the method comprises:
determining a first difference image according to the pixel value of each pixel point of the current frame image and the difference value of the pixel value of the corresponding pixel point of the previous frame image of the current frame image;
Taking absolute values of pixel values of each pixel point in the first difference image to determine a corresponding second difference image;
taking the absolute value of the pixel value of each pixel point in the image subjected to the filtering processing of the first difference image to obtain a first filtering image; filtering the second difference image to obtain a second filtered image; taking the pixel value difference value of each corresponding pixel point in the second filter image and the first filter image as the noise value of each pixel point;
and determining a target corresponding relation between the brightness value and the noise value according to the brightness value of each pixel point in the current frame image and the noise value of each pixel point, and updating the stored corresponding relation by adopting the target corresponding relation.
Further, the step of taking the pixel value difference value of each corresponding pixel point in the second filtered image and the first filtered image as the noise value of each pixel point includes:
dividing the first filtering image and the second filtering image into a plurality of second noise reduction areas according to the same dividing mode;
and determining a pixel value difference value of each corresponding pixel point in the second noise reduction region aiming at each corresponding second noise reduction region, and taking an average value of the pixel value difference values of each pixel point as a noise value of each pixel point in the second noise reduction region.
Further, the determining the target correspondence between the luminance value and the noise value according to the luminance value of each pixel point in the current frame image and the noise value of each pixel point includes:
dividing the current frame image into a plurality of second noise reduction areas according to the same dividing mode as the filtering image;
for each second noise reduction region, determining an average brightness value of the second noise reduction region according to the brightness value of each pixel point in the second noise reduction region;
and determining the target corresponding relation between the brightness value and the noise value according to the average value of the average brightness value and the pixel value difference value of each second noise reduction area.
Further, the target correspondence between the luminance value and the noise value satisfies a linear relationship.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (11)
1. A method of noise processing a video image, the method comprising:
determining a target average brightness value of each first noise reduction area of the current frame image;
determining a target noise value of each first noise reduction region of the current frame image according to the target average brightness value of each first noise reduction region and the corresponding relation between the pre-stored average brightness value and the noise value;
According to the target noise value of each first noise reduction region of the current frame image, carrying out noise reduction treatment on each first noise reduction region of the current frame image;
the method further comprises the steps of:
determining a first difference image according to the pixel value of each pixel point of the current frame image and the difference value of the pixel value of the corresponding pixel point of the previous frame image of the current frame image;
taking absolute values of pixel values of each pixel point in the first difference image to determine a corresponding second difference image;
taking the absolute value of the pixel value of each pixel point in the image subjected to the filtering processing of the first difference image to obtain a first filtering image; filtering the second difference image to obtain a second filtered image; taking the pixel value difference value of each corresponding pixel point in the second filter image and the first filter image as the noise value of each pixel point;
and determining a target corresponding relation between the brightness value and the noise value according to the brightness value of each pixel point in the current frame image and the noise value of each pixel point, and updating the stored corresponding relation by adopting the target corresponding relation.
2. The method of claim 1, wherein determining a target average luminance value for each first noise reduction region of the current frame image comprises:
For each first noise reduction region, determining a target noise reduction range containing the first noise reduction region according to a preset region range, and determining a target average brightness value of the first noise reduction region according to the brightness value of each pixel point contained in the target noise reduction range.
3. The method of claim 1, wherein said differencing the pixel value of each corresponding pixel in the second filtered image and the first filtered image as the noise value of each pixel comprises:
dividing the first filtering image and the second filtering image into a plurality of second noise reduction areas according to the same dividing mode;
and determining a pixel value difference value of each corresponding pixel point in the second noise reduction region aiming at each corresponding second noise reduction region, and taking an average value of the pixel value difference values of each pixel point as a noise value of each pixel point in the second noise reduction region.
4. The method of claim 1, wherein determining the target correspondence between the luminance value and the noise value according to the luminance value of each pixel in the current frame image and the noise value of each pixel comprises:
dividing the current frame image into a plurality of second noise reduction areas according to the same dividing mode as the filtering image;
For each second noise reduction region, determining an average brightness value of the second noise reduction region according to the brightness value of each pixel point in the second noise reduction region;
and determining the target corresponding relation between the brightness value and the noise value according to the average value of the average brightness value and the pixel value difference value of each second noise reduction area.
5. The method of claim 4, wherein the target correspondence of luminance values and noise values satisfies a linear relationship.
6. A noise processing apparatus for video images, the apparatus comprising:
the determining module is used for determining a target average brightness value of each first noise reduction area of the current frame image; determining a target noise value of each first noise reduction region of the current frame image according to the target average brightness value of each first noise reduction region and the corresponding relation between the pre-stored average brightness value and the noise value;
the noise reduction module is used for carrying out noise reduction processing on each first noise reduction area of the current frame image according to the target noise value of each first noise reduction area of the current frame image;
the determining module is further configured to determine a first difference image according to a difference between a pixel value of each pixel point of the current frame image and a pixel value of a corresponding pixel point of a previous frame image of the current frame image; taking absolute values of pixel values of each pixel point in the first difference image to determine a corresponding second difference image;
The apparatus further comprises:
the filtering module is used for obtaining a first filtering image by taking the absolute value of the pixel value of each pixel point in the image subjected to the filtering processing of the first difference image; filtering the second difference image to obtain a second filtered image; taking the pixel value difference value of each corresponding pixel point in the second filter image and the first filter image as the noise value of each pixel point;
and the updating module is used for determining a target corresponding relation between the brightness value and the noise value according to the brightness value of each pixel point in the current frame image and the noise value of each pixel point, and updating the stored corresponding relation by adopting the target corresponding relation.
7. The apparatus according to claim 6, wherein the determining module is specifically configured to determine, for each first noise reduction region, a target noise reduction range including the first noise reduction region according to a preset region range, and determine, according to a luminance value of each pixel point included in the target noise reduction range, a target average luminance value of the first noise reduction region.
8. The apparatus according to claim 6, wherein the filtering module is specifically configured to divide the first filtered image and the second filtered image into a plurality of second noise reduction regions in the same division manner; and determining a pixel value difference value of each corresponding pixel point in the second noise reduction region aiming at each corresponding second noise reduction region, and taking an average value of the pixel value difference values of each pixel point as a noise value of each pixel point in the second noise reduction region.
9. The apparatus according to claim 6, wherein the updating module is specifically configured to divide the current frame image into a plurality of second noise reduction areas according to the same division manner as the filtered image; for each second noise reduction region, determining an average brightness value of the second noise reduction region according to the brightness value of each pixel point in the second noise reduction region; and determining the target corresponding relation between the brightness value and the noise value according to the average value of the average brightness value and the pixel value difference value of each second noise reduction area.
10. An electronic device comprising a processor and a memory for storing program instructions, the processor being adapted to implement the steps of the noise processing method of a video image according to any of claims 1-5 when executing a computer program stored in the memory.
11. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the steps of the noise processing method of a video image according to any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010318649.3A CN111507923B (en) | 2020-04-21 | 2020-04-21 | Noise processing method, device, equipment and medium for video image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010318649.3A CN111507923B (en) | 2020-04-21 | 2020-04-21 | Noise processing method, device, equipment and medium for video image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111507923A CN111507923A (en) | 2020-08-07 |
| CN111507923B true CN111507923B (en) | 2023-09-12 |
Family
ID=71864792
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010318649.3A Active CN111507923B (en) | 2020-04-21 | 2020-04-21 | Noise processing method, device, equipment and medium for video image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111507923B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112085682B (en) * | 2020-09-11 | 2023-08-22 | 成都国科微电子有限公司 | Image noise reduction method and device, electronic equipment and storage medium |
| CN112055255B (en) * | 2020-09-15 | 2022-07-05 | 深圳创维－Rgb电子有限公司 | Shooting image quality optimization method and device, smart television and readable storage medium |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104754181A (en) * | 2013-12-30 | 2015-07-01 | 浙江大华技术股份有限公司 | Video noise estimating method and device |
| CN106303157A (en) * | 2016-08-31 | 2017-01-04 | 广州市百果园网络科技有限公司 | A kind of vedio noise reduction processing method and vedio noise reduction processing means |
| WO2017101489A1 (en) * | 2015-12-14 | 2017-06-22 | 乐视控股（北京）有限公司 | Method and device for image filtering |
| CN106920216A (en) * | 2015-12-25 | 2017-07-04 | 展讯通信（上海）有限公司 | A kind of method and device for eliminating picture noise |
| US9715720B1 (en) * | 2014-12-15 | 2017-07-25 | Marvell International Ltd. | System and method for reducing image noise |
| CN107979712A (en) * | 2016-10-20 | 2018-05-01 | 上海富瀚微电子股份有限公司 | A kind of vedio noise reduction method and device |
| CN108109123A (en) * | 2017-12-21 | 2018-06-01 | 成都微光集电科技有限公司 | A kind of image de-noising method |
| CN108810320A (en) * | 2018-06-01 | 2018-11-13 | 深圳市商汤科技有限公司 | Picture quality method for improving and device |
| CN108805851A (en) * | 2017-04-26 | 2018-11-13 | 杭州海康威视数字技术股份有限公司 | A kind of appraisal procedure and device of image noise in time domain |
| CN109639982A (en) * | 2019-01-04 | 2019-04-16 | Oppo广东移动通信有限公司 | A kind of image denoising method, device, storage medium and terminal |
| CN109859126A (en) * | 2019-01-17 | 2019-06-07 | 浙江大华技术股份有限公司 | A kind of vedio noise reduction method, apparatus, electronic equipment and storage medium |
| CN110445953A (en) * | 2019-08-02 | 2019-11-12 | 浙江大华技术股份有限公司 | Reduce method, apparatus, electronic equipment and the storage device of dynamic fringe noise |
| CN110689496A (en) * | 2019-09-25 | 2020-01-14 | 北京迈格威科技有限公司 | Method and device for determining noise reduction model, electronic equipment and computer storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7203378B2 (en) * | 2002-09-27 | 2007-04-10 | Chui-Kuei Chiu | Accelerative noise filtering method for image data |
| JP4457346B2 (en) * | 2004-11-12 | 2010-04-28 | ノーリツ鋼機株式会社 | Image noise removal method |
-
2020
- 2020-04-21 CN CN202010318649.3A patent/CN111507923B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104754181A (en) * | 2013-12-30 | 2015-07-01 | 浙江大华技术股份有限公司 | Video noise estimating method and device |
| US9715720B1 (en) * | 2014-12-15 | 2017-07-25 | Marvell International Ltd. | System and method for reducing image noise |
| WO2017101489A1 (en) * | 2015-12-14 | 2017-06-22 | 乐视控股（北京）有限公司 | Method and device for image filtering |
| CN106920216A (en) * | 2015-12-25 | 2017-07-04 | 展讯通信（上海）有限公司 | A kind of method and device for eliminating picture noise |
| CN106303157A (en) * | 2016-08-31 | 2017-01-04 | 广州市百果园网络科技有限公司 | A kind of vedio noise reduction processing method and vedio noise reduction processing means |
| CN107979712A (en) * | 2016-10-20 | 2018-05-01 | 上海富瀚微电子股份有限公司 | A kind of vedio noise reduction method and device |
| CN108805851A (en) * | 2017-04-26 | 2018-11-13 | 杭州海康威视数字技术股份有限公司 | A kind of appraisal procedure and device of image noise in time domain |
| CN108109123A (en) * | 2017-12-21 | 2018-06-01 | 成都微光集电科技有限公司 | A kind of image de-noising method |
| CN108810320A (en) * | 2018-06-01 | 2018-11-13 | 深圳市商汤科技有限公司 | Picture quality method for improving and device |
| CN109639982A (en) * | 2019-01-04 | 2019-04-16 | Oppo广东移动通信有限公司 | A kind of image denoising method, device, storage medium and terminal |
| CN109859126A (en) * | 2019-01-17 | 2019-06-07 | 浙江大华技术股份有限公司 | A kind of vedio noise reduction method, apparatus, electronic equipment and storage medium |
| CN110445953A (en) * | 2019-08-02 | 2019-11-12 | 浙江大华技术股份有限公司 | Reduce method, apparatus, electronic equipment and the storage device of dynamic fringe noise |
| CN110689496A (en) * | 2019-09-25 | 2020-01-14 | 北京迈格威科技有限公司 | Method and device for determining noise reduction model, electronic equipment and computer storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 广义非局部均值算法的图像去噪;郭红涛等;计算机应用研究;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111507923A (en) | 2020-08-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109146816B (en) | Image filtering method and device, electronic equipment and storage medium | |
| CN111507923B (en) | Noise processing method, device, equipment and medium for video image | |
| CN110473238B (en) | Image registration method and device, electronic equipment and storage medium | |
| CN111028179B (en) | Stripe correction method and device, electronic equipment and storage medium | |
| CN111080654B (en) | Image lesion region segmentation method and device and server | |
| CN109584175B (en) | Image processing method and device | |
| CN106504205A (en) | A kind of image defogging method and terminal | |
| CN111368587A (en) | Scene detection method and device, terminal equipment and computer readable storage medium | |
| CN110211082B (en) | Image fusion method and device, electronic equipment and storage medium | |
| CN110400271B (en) | Stripe non-uniformity correction method and device, electronic equipment and storage medium | |
| CN111107274B (en) | Image brightness statistical method and imaging device | |
| CN115457063A (en) | Method, device and equipment for extracting edge of circular hole of PCB (printed Circuit Board) and storage medium | |
| CN113920022A (en) | Image optimization method and device, terminal equipment and readable storage medium | |
| CN109753957B (en) | Image significance detection method and device, storage medium and electronic equipment | |
| CN113628259A (en) | Image registration processing method and device | |
| CN106971386B (en) | Method and device for judging image integrity and page loading degree and client equipment | |
| CN111242051B (en) | Vehicle identification optimization method, device and storage medium | |
| CN115278104B (en) | Image brightness adjustment method and device, electronic equipment and storage medium | |
| CN104754181B (en) | A kind of video noise estimation method and device | |
| CN116017129A (en) | Method, device, system, equipment and medium for adjusting angle of light supplementing lamp | |
| CN114998172A (en) | Image processing method and related system | |
| CN111986144B (en) | Image blurring judging method, device, terminal equipment and medium | |
| CN112146834B (en) | Method and device for measuring structural vibration displacement | |
| CN114648455A (en) | Ultrasonic image processing method, electronic device and storage medium | |
| CN115830048A (en) | Image edge detection method and device and related equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |