CN111598909B

CN111598909B - Frequency domain characteristic judgment method of image signal and related equipment

Info

Publication number: CN111598909B
Application number: CN202010384362.0A
Authority: CN
Inventors: 张鑫; 陈欢; 马维维; 温瑞丹; 魏道敏
Original assignee: Spreadtrum Communications Shanghai Co Ltd
Current assignee: Spreadtrum Communications Shanghai Co Ltd
Priority date: 2020-05-08
Filing date: 2020-05-08
Publication date: 2023-03-14
Anticipated expiration: 2040-05-08
Also published as: CN111598909A

Abstract

The embodiment of the application provides a method for judging frequency domain characteristics of an image signal and related equipment, and the method comprises the following steps: taking the current pixel point position to be processed as the center, acquiring pixel data in _ data [ i ] [ j ] of a neighborhood n multiplied by n of the pixel point to be processed of the image signal, and taking in _ data [ (n-1)/2 ] [ (n-1)/2 ] as the current pixel point to be detected; extracting pixel data of two templates with different sizes from the pixel data of the neighborhood n multiplied by n to convert the pixel data into two groups of brightness information, and respectively calculating the variance of the two groups of brightness information according to the two groups of brightness information to obtain two variance values; and judging the frequency domain characteristics of the current pixel point to be processed according to the relationship between the two variance values and a plurality of preset thresholds. The technical scheme provided by the application has the advantages of small calculated amount, fine frequency domain feature division of the image and capability of realizing point-by-point judgment and being beneficial to actual engineering realization.

Description

Frequency domain characteristic judgment method of image signal and related equipment

Technical Field

The present application relates to the field of image processing technologies, and in particular, to a method for determining frequency domain characteristics of an image signal and a related device.

Background

At present, a general edge detection method is used, only an edge region and a non-edge region can be detected, but the frequency domain characteristics of the current pixel point cannot be divided in detail, and the detection result is easily influenced by factors such as noise and gray level gradient conditions.

The currently commonly used edge detection methods include a sobel operator, a laplacian operator, a canny operator, a roberts operator and a prewitt operator. The sobel operator detection method has a good effect on processing images with gradually changed gray levels and more noises, but the operator is not very accurate in edge positioning, and is a more common edge detection method when the requirement on precision is not very high. The canny method is not easily disturbed by noise, can detect a true weak edge, detects a strong edge and a weak edge respectively using two different thresholds, and includes the weak edge in an output image when the weak edge and the strong edge are connected. The laplacian operator method is sensitive to noise, so the laplacian operator is rarely used for detecting edges, and is used for judging whether edge pixels are regarded as bright areas or dark areas of an image.

In addition, the image frequency division condition can be judged by using a discrete cosine transform mode, for example, image data is input according to blocks, then discrete cosine change is carried out on the data, and then a frequency division result of the current pixel point position is determined by comparing a discrete cosine transform coefficient statistical mode with a set related threshold value.

In the method, in the using process, the result can be detected only after the discrete cosine transform operation is carried out on the neighborhood information of each pixel point, so that the method has higher computational complexity.

Disclosure of Invention

The embodiment of the application discloses a method for judging the frequency domain characteristics of an image signal, which can realize the judgment of the frequency domain characteristics of the image, has the advantages of small calculated amount, high speed and more precise frequency domain division result, can realize point-by-point detection and is favorable for the realization and the use in engineering.

The first aspect of the present embodiment discloses a method for determining frequency domain characteristics of an image signal, including:

taking the current pixel point position to be processed as a center, acquiring pixel data in _ data [ i ] [ j ] of a neighborhood n multiplied by n of the pixel point to be processed of the image signal, wherein i =0,1, \8230;, n-1; j =0,1, \8230, n-1, then in _ data [ (n-1)/2 ] [ (n-1)/2 ] is the current pixel point to be detected;

extracting pixel data of two templates with different sizes from the pixel data of the neighborhood n multiplied by n to convert the pixel data into two groups of brightness information, and respectively calculating the variance of the two groups of brightness information according to the two groups of brightness information to obtain two variance values;

and judging the frequency domain characteristics of the current pixel point to be processed according to the relationship between the two variance values and a plurality of preset thresholds.

In a second aspect, a terminal is provided, which includes:

the image processing device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring pixel data in _ data [ i ] [ j ] of a neighborhood n multiplied by n of a pixel point to be processed of the image signal by taking the current pixel point position to be processed as a center, wherein i =0,1, \8230;, n-1; j =0,1, \8230, if n-1, in _ data [ (n-1)/2 ] [ (n-1)/2 ] is the current pixel point to be detected;

the processing unit is used for extracting two pieces of template pixel data with different sizes from the pixel data of the neighborhood nxn to convert the pixel data into two pieces of brightness information, and calculating the variance of the two pieces of brightness information respectively according to the two pieces of brightness information to obtain two variance values; judging the frequency domain characteristics of the current pixel point to be processed according to the relationship between the two variance values and a plurality of preset thresholds; and selecting corresponding strength and processing strategy for relevant processing according to the judged frequency domain characteristic result.

In a third aspect, there is provided a terminal comprising a processor, a memory, a communications interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps in the method of the first aspect.

A fourth aspect of embodiments of the present application discloses a computer-readable storage medium, which is characterized by storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the method of the first aspect.

A fifth aspect of embodiments of the present application discloses a computer program product, wherein the computer program product comprises a non-transitory computer-readable storage medium storing a computer program, the computer program being operable to cause a computer to perform some or all of the steps as described in the first aspect of embodiments of the present application. The computer program product may be a software installation package.

By implementing the embodiment of the application, the technical scheme provided by the embodiment of the application has higher detection result accuracy, can effectively distinguish the frequency domain grade of the current pixel point to be processed, can also effectively judge the flat area near the edge, can further perform related processing such as denoising and the like by using corresponding strength according to the frequency domain characteristics judged by refinement, and uses different denoising strategies to process the flat area near the edge, so that the image data can keep the existence of detail information while ensuring the noise removal by using the method, and the frequency division method can greatly improve the effect of a related denoising algorithm; in addition, the embodiment of the application judges in a mode of setting a threshold value, so that the flexibility is higher; in the process of judging the frequency domain, operations such as wavelet decomposition and the like are not needed, and the calculation complexity is relatively low.

Drawings

The drawings used in the embodiments of the present application are described below.

Fig. 1 is a schematic structural diagram of a terminal according to an embodiment of the present application;

fig. 2 is a schematic flowchart of a method for determining frequency domain characteristics of an image signal according to an embodiment of the present disclosure;

FIG. 2a is a diagram of 11 × 11 neighborhood data;

fig. 3 is a schematic flowchart of a method for determining frequency domain characteristics of an image signal according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a terminal provided in an embodiment of the present application;

fig. 5 is a hardware schematic diagram of another terminal according to an embodiment of the present application.

Detailed Description

The embodiments of the present application are described below with reference to the drawings.

The term "and/or" in this application is only one kind of association relationship describing the association object, and means that there may be three kinds of relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein indicates that the former and latter associated objects are in an "or" relationship.

The "plurality" appearing in the embodiments of the present application means two or more. The descriptions of the first, second, etc. appearing in the embodiments of the present application are only for illustrating and differentiating the objects, and do not represent the order or the particular limitation of the number of the devices in the embodiments of the present application, and do not constitute any limitation to the embodiments of the present application. The term "connection" in the embodiment of the present application refers to various connection manners such as direct connection or indirect connection, so as to implement communication between devices, which is not limited in this embodiment of the present application.

A terminal in the embodiments of the present application may refer to various forms of UE, access terminal, subscriber unit, subscriber station, mobile station, MS (mobile station), remote station, remote terminal, mobile device, user terminal, terminal device (terminal equipment), wireless communication device, user agent, or user equipment. The terminal device may also be a cellular phone, a cordless phone, an SIP (session initiation protocol) phone, a WLL (wireless local loop) station, a PDA (personal digital assistant) with a wireless communication function, a handheld device with a wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a future 5G network or a terminal device in a future evolved PLMN (public land mobile network, chinese), and the like, which are not limited in this embodiment.

Referring to fig. 1, fig. 1 is a schematic structural diagram of a terminal disclosed in an embodiment of the present application, the terminal 100 includes a storage and processing circuit 110, and a sensor 170 connected to the storage and processing circuit 110, the sensor 170 may include a camera, a distance sensor, a gravity sensor, and the like, the electronic device may include two transparent display screens, the transparent display screens are disposed on the back and the front of the electronic device, and some or all of the components between the two transparent display screens may also be transparent, so that the electronic device may be a transparent electronic device in visual effect, and if some of the components are transparent, the electronic device may be a hollow electronic device. Wherein:

the terminal 100 may include control circuitry, which may include storage and processing circuitry 110. The storage and processing circuit 110 may be a memory, such as a hard disk drive memory, a non-volatile memory (e.g., a flash memory or other electronically programmable read only memory used to form a solid state drive, etc.), a volatile memory (e.g., a static or dynamic random access memory, etc.), etc., and the embodiments of the present application are not limited thereto. Processing circuitry in the storage and processing circuitry 110 may be used to control the operation of the terminal 100. The processing circuitry may be implemented based on one or more microprocessors, microcontrollers, digital signal processors, baseband processors, power management units, audio codec chips, application specific integrated circuits, display driver integrated circuits, and the like.

The storage and processing circuitry 110 may be used to run software in the terminal 100, such as an Internet browsing application, a Voice Over Internet Protocol (VOIP) telephone call application, an email application, a media playing application, operating system functions, and so forth. Such software may be used to perform control operations such as camera-based image capture, ambient light measurement based on an ambient light sensor, proximity sensor measurement based on a proximity sensor, information display functionality based on status indicators such as status indicator lights of light emitting diodes, touch event detection based on a touch sensor, functionality associated with displaying information on multiple (e.g., layered) display screens, operations associated with performing wireless communication functions, operations associated with collecting and generating audio signals, control operations associated with collecting and processing button press event data, and other functions in the terminal 100, for example, and the like, embodiments of the present application are not limited.

The terminal 100 may include an input-output circuit 150. The input-output circuit 150 may be used to enable the terminal 100 to input and output data, i.e., to allow the terminal 100 to receive data from external devices and also to allow the terminal 100 to output data from the terminal 100 to external devices. The input-output circuit 150 may further include a sensor 170. Sensor 170 vein identification module, can also include ambient light sensor, proximity sensor based on light and electric capacity, fingerprint identification module, touch sensor (for example, based on light touch sensor and/or capacitanc touch sensor, wherein, touch sensor can be touch-control display screen's partly, also can regard as a touch sensor structure independent utility), acceleration sensor, the camera, and other sensors etc. the camera can be leading camera or rear camera, the fingerprint identification module can integrate in the display screen below, be used for gathering the fingerprint image, the fingerprint identification module can be: optical fingerprint module, etc., and is not limited herein. The front camera can be arranged below the front display screen, and the rear camera can be arranged below the rear display screen. Certainly, the front camera or the rear camera may not be integrated with the display screen, and certainly, in practical application, the front camera or the rear camera may also be of a lifting structure, and the specific structure of the front camera or the rear camera is not limited in the specific embodiment of the present application.

Input-output circuit 150 may also include one or more display screens, and when multiple display screens are provided, such as 2 display screens, one display screen may be provided on the front of the electronic device and another display screen may be provided on the back of the electronic device, such as display screen 130. The display 130 may include one or a combination of liquid crystal display, transparent display, organic light emitting diode display, electronic ink display, plasma display, and display using other display technologies. The display screen 130 may include an array of touch sensors (i.e., the display screen 130 may be a touch display screen). The touch sensor may be a capacitive touch sensor formed by a transparent touch sensor electrode (e.g., an Indium Tin Oxide (ITO) electrode) array, or may be a touch sensor formed using other touch technologies, such as acoustic wave touch, pressure sensitive touch, resistive touch, optical touch, and the like, and the embodiments of the present application are not limited thereto.

The terminal 100 can also include an audio component 140. Audio component 140 may be used to provide audio input and output functionality for terminal 100. The audio components 140 in the terminal 100 may include a speaker, a microphone, a buzzer, a tone generator, and other components for generating and detecting sound.

The communication circuit 120 can be used to provide the terminal 100 with the capability to communicate with external devices. The communication circuit 120 may include analog and digital input-output interface circuits, and wireless communication circuits based on radio frequency signals and/or optical signals. The wireless communication circuitry in communication circuitry 120 may include radio-frequency transceiver circuitry, power amplifier circuitry, low noise amplifiers, switches, filters, and antennas. For example, the wireless Communication circuitry in the Communication circuitry 120 may include circuitry to support Near Field Communication (NFC) by transmitting and receiving Near Field coupled electromagnetic signals. For example, the communication circuit 120 may include a near field communication antenna and a near field communication transceiver. The communications circuitry 120 may also include a cellular telephone transceiver and antenna, a wireless local area network transceiver circuitry and antenna, and so forth.

The terminal 100 may further include a battery, a power management circuit, and other input-output units 160. The input-output unit 160 may include buttons, joysticks, click wheels, scroll wheels, touch pads, keypads, keyboards, cameras, light emitting diodes and other status indicators, and the like.

A user may input commands through input-output circuitry 150 to control operation of terminal 100 and may use output data of input-output circuitry 150 to enable receipt of status information and other outputs from terminal 100.

Referring to fig. 2, fig. 2 provides a method for determining frequency domain characteristics of an image signal, which may be performed by the terminal shown in fig. 1, and the method shown in fig. 2 includes the following steps:

and S200, the terminal takes the current pixel point position to be processed as the center to acquire pixel data in _ data [ i ] [ j ] of a neighborhood n multiplied by n of the pixel point to be processed of the image signal.

Wherein i =0,1, \8230, n-1; j =0,1, \8230;, n-1, then in _ data [ (n-1)/2 ] [ (n-1)/2 ] is the current pixel point to be detected.

Referring to fig. 2a, fig. 2a is a schematic diagram of 11 × 11 neighborhood data, as shown in fig. 2a, wherein the pixel point to be detected in the neighborhood is in _ data [5] [5], that is, the R channel pixel point in the center position.

Certainly, in practical applications, the pixel to be processed may also be any one of a B-channel pixel and a G-channel pixel.

Step S201, a terminal extracts pixel data of two templates with different sizes from the pixel data of the neighborhood n multiplied by n to convert the pixel data into two groups of brightness information, and variance of the two groups of brightness information is respectively calculated according to the two groups of brightness information to obtain two variance values;

step S202, the terminal judges the frequency domain characteristics of the current pixel point to be processed according to the relation between the variance value of the two groups of brightness information and a plurality of preset thresholds.

According to the technical scheme, a terminal takes a current pixel point to be processed as a center, pixel data in _ data [ i ] [ j ] of a neighborhood n multiplied by n of the pixel point to be processed of an image signal is obtained, pixel data of two templates with different sizes are extracted from the pixel data of the neighborhood n multiplied by n and converted into two groups of brightness information, variance of the two groups of brightness information is calculated according to the two groups of brightness information to obtain two variance values, the terminal judges frequency domain characteristics of the current pixel point to be processed according to the relation between the variance values of the two groups of brightness information and a plurality of preset threshold values, compared with an existing judging method such as a discrete cosine transformation method, the method only needs to calculate the mean value and the variance of the brightness information, the calculated amount is very small, therefore, the calculated amount of frequency domain characteristics is reduced, the speed of judging the frequency domain characteristics is improved, meanwhile, the judging result of the frequency domain characteristics is more precise, and accurate, the technical scheme can realize point-by-point judgment of the image data, and is beneficial to application in practical engineering.

In an alternative arrangement, the first and second electrodes may be,

the two templates with different sizes are divided into a large-size template and a small-size template, wherein the large-size template is n × n pixel data in a neighborhood, the small-size template is m × m pixel data in the neighborhood, the pixel data of the m × m template is located in a central area of the n × n template pixel data, n and m are both odd numbers, and the corresponding relation between m and n is n =2 × m-3 and m is more than or equal to 5.

Taking the 11 × 11 neighborhood data as shown in fig. 2a as an example, the large-size template is 11 × 11 pixel data, and the small-size template is 7 × 7 pixel data in the middle of the large-size template.

In an alternative, the converting the data of the neighborhood n × n into two sets of luminance information by using pixel data of two size templates specifically includes:

converting the large template n multiplied by n pixel data of the neighborhood into corresponding brightness information in a Gaussian filtering mode by taking 3 multiplied by 3 as a unit;

the pixel data of the small template mxm of the neighborhood is converted into corresponding luminance information by means of gaussian filtering in units of 3 × 3.

The kernel function of the Gaussian filter used in the process of converting the luminance information to the neighborhood pixel information is

The luminance information variance of the large-size template and the luminance information variance of the small-size template are calculated by specific formulas.

The variance of the luminance information for the large-size template transformation is:

wherein mean _ b is the mean value of lum _ data _ b [ i ] [ j ]:

the variance of the luminance information for the small-size template transform is:

wherein mean _ is the mean of lum _ data _ [ ] [ ]:

the lumdataj is a set of luminance information converted from the large-size template pixel data, and the lumdataj is a set of luminance information converted from the small-size template pixel data.

Taking the pixel size shown in fig. 2a as an example, the corresponding calculation relationship between the large-size template 11 × 11bayer data of the current pixel neighborhood to be detected and the converted luminance information lum _ b is as follows:

wherein in _ is 11 × 11 neighborhood pixel data of a pixel point to be processed, namely large-size template pixel data of a neighborhood of the pixel point to be processed, lum _ data _ b [ i ] [ j ] is a corresponding brightness information value converted by input neighborhood large-size template bayer pixel data, and i =0,1, \82304; (4); j =0,1, \ 82304; 4.

Taking the pixel size shown in fig. 2a as an example, the corresponding calculation relationship between the small-size template 7 × 7bayer data in the current pixel neighborhood to be detected and the converted luminance information lum _ s is as follows:

wherein in _ t is the 7 × 7 small-sized template pixel data of the neighborhood of the pixel point to be processed, lum _ data _ s [ i ] [ j ] is the corresponding brightness information value converted by the input neighborhood small-size template bayer pixel data, i =0,1, \ 82304; j =0,1, \ 82304; 4.

In an alternative arrangement, the first and second electrodes may be,

the plurality of thresholds includes: a small-size template-dependent division threshold sigma _ s and 3 large-size template-dependent division thresholds: sigma _0, sigma _1and sigma _2, and sigma _0 and wavelet _1and sigma _2 are constructed.

In an optional scheme, the implementation method of step S202 may specifically include:

if var _ s is less than sigma _ s and var _ b is less than sigma _0, determining that the current pixel point to be processed is a flat area;

if var _ s is less than sigma _ s and var _ b is more than or equal to sigma _0, determining that the current pixel point to be processed is a flat area near the edge;

if var _ s is larger than or equal to sigma _ s and sigma _0 is larger than or equal to var _ b and smaller than sigma _1, determining the current pixel point to be processed as a secondary flat area;

if var _ s is larger than or equal to sigma _ s and sigma _1 is larger than or equal to var _ b and smaller than sigma _2, determining the current pixel point to be processed as a secondary high-frequency area;

and if var _ s is larger than or equal to sigma _ s and var _ b is larger than or equal to sigma _2, determining that the current pixel point to be processed is a high-frequency area.

In an optional scheme, the filtering and denoising according to the frequency domain characteristic judgment result of the current pixel point to be processed and the denoising strategy corresponding to the frequency domain characteristic specifically includes:

when the frequency domain characteristics of the pixel points to be processed are judged to be flat areas near the edges, only pixel points of the same channel of the small-size template neighborhood data of m multiplied by m are selected to carry out filtering and denoising in a weighted average mode; and when the frequency domain characteristics are judged to be the rest areas, selecting the same-channel pixel points of the n multiplied by n large-size template neighborhood data to carry out filtering and denoising in a weighted average mode.

The denoising method can be selected according to the frequency domain characteristics of the current pixel points to be processed, so that the frequency domain characteristics of the image pixels are matched with the denoising method, the denoising effect of the image is further improved, and therefore the frequency domain characteristic judging method has the advantage of improving the image processing effect.

The setting of the filtering and denoising strength in the weighted average method may specifically include:

when filtering, the denoising strength is correspondingly set according to the frequency division result, and the method specifically comprises the following steps: the denoising strength of the flat region is more than the denoising strength of the sub-flat region, more than the denoising strength of the sub-high frequency, more than the denoising strength of the high frequency, and the denoising strength of the flat region is approximately equal to the denoising strength of the flat region near the edge.

Referring to fig. 3, fig. 3 is a frequency domain characteristic determining method of an image signal according to an embodiment of the present application, where the method is executed by a terminal shown in fig. 1, and a current pixel point to be processed in the method is in _ data [5] [5] as shown in fig. 2 a; the method, as shown in fig. 3, includes the following steps:

s300, inputting n multiplied by n neighborhood pixel data;

inputting neighborhood data of current position pixel n multiplied by n to be processed, and storing into array in _ data [ i ] [ j ], wherein in _ data [ (n-1)/2 ] [ (n-1)/2 ] is current pixel point.

Wherein i =0,1, \8230, n-1; j =0,1, \8230;, n-1. The input n × n neighborhood pixel data may be specifically as shown in fig. 2a, where n =11 is taken as an example.

And S301, calculating the brightness of the size template.

When the current pixel frequency domain information is determined, firstly, the input bayer domain pixel data shown in fig. 2a needs to be gaussian filtered in a unit of 3 × 3 size, and converted into luminance information. In order to improve the detection accuracy of frequency division information, in this embodiment, when luminance information calculation is performed on bayer data, template data with different sizes are respectively selected for conversion, so that gaussian filtering is performed on input 11 × 11bayer data by using templates with different sizes and respectively using pixel data with 3 × 3 size as a unit to obtain 2 groups of luminance information with 5 × 5 size, where the large template data and the small template data correspond to each other as follows:

in_data_t[i][j]＝in_data[i+2][j+2]

wherein, i =0,1, \8230;, 6; j =0,1, \8230;, 6. Namely, the used neighborhood large-size template data is 11 × 11, the neighborhood small-size template data is 7 × 7, and the small-size template data is 7 × 7bayer domain data among the selected large-size template data.

As shown in fig. 2a, for example, with n =11, in _ data [5]][5]For the current pixel point to be processed, the large-size template data selects 11 × 11bayer domain data for brightness conversion, the small-size template data selects 7 × 7bayer domain data for brightness conversion, and the kernel function of gaussian filtering for brightness information conversion is

The corresponding calculation relationship between the 11 × 11bayer data of the current pixel to be detected and the converted luminance information lum _ data _ b is as follows:

wherein in _ data is 11 × 11 neighborhood pixel data of a pixel point to be processed, namely large-size template pixel data of a neighborhood of the pixel point to be processed, lum _ data _ b [ i ] [ j ] is a corresponding brightness information value converted by input neighborhood large-size template bayer pixel data, and i =0, 1.. 4; j =0,1.. 4.

The corresponding calculation relationship between the 7 × 7bayer data of the pixel small-size template at the current position to be detected and the converted luminance information lum _ s is as follows:

wherein in _ data _ t is 7 × 7 small-size template pixel data of a neighborhood of a pixel point to be processed, lum _ data _ s [ i ] [ j ] is a corresponding brightness information value converted by input neighborhood small-size template bayer pixel data, and i =0, 1.. 4; j =0,1, \ 82304; 4.

Step S302, two variances of the size template are calculated.

And respectively calculating the variance of the luminance information converted by the size template data:

the variance of the luminance information for the large size data template transformation is:

wherein mean _ b is the mean value of lum _ data _ b [ i ] [ j ]:

the variance of luminance information for small size data template conversion is:

wherein mean _ s is the mean of lum _ data _ s [ i ] [ j ]:

and step S303, judging a frequency division result according to the corresponding relation between the set threshold and the variance.

The method specifically comprises the following steps:

3 related division thresholds sigma _0, sigma _1and sigma _2 are set for a large-size template, the threshold relation is guaranteed to be sigma _0 and sigma _1and sigma _2, and 1 related threshold sigma _ s is set for a small-size template. And then dividing the frequency domain of the current pixel point according to the set threshold. The specific division is shown as the following formula, namely if var _ s is smaller than sigma _ s and var _ b is smaller than sigma _0, freq _ index =0 is set; if var _ s is smaller than sigma _ s and var _ b is greater than or equal to sigma _0, setting freq _ index =1; if var _ s is greater than or equal to sigma _ s and var _ b is greater than or equal to sigma _0 and smaller than sigma _1, setting freq _ index =2; if var _ s is greater than or equal to sigma _ s and var _ b is greater than or equal to sigma _1 and smaller than sigma _2, setting freq _ index =3; and if var _ s is greater than or equal to sigma _ s and var _ b is greater than or equal to sigma _2, setting freq _ index =4.

And S304, selecting a corresponding denoising strategy according to the frequency division result.

The method specifically comprises the following steps:

and outputting an identification freq _ index of the current pixel frequency division judgment, 0: a flat region; 1: a flat region near the edge; 2: a sub-flat region; 3: a second high frequency region; 4: a high frequency region.

For a flat area near the edge of freq _ index =1, because the area is relatively close to the edge, only 9 pixel points of the same channel in the neighborhood of 7 × 7 small-size template bayer data are selected for filtering and denoising; and in other areas, 25 pixel points of the same channel in the neighborhood of 11 multiplied by 11 large-size template bayer data are selected for filtering and denoising. When filtering and denoising are carried out, the specific denoising intensity is correspondingly set according to the frequency division result of the pixel point to be processed.

The setting of the denoising strength corresponding to the frequency division result may specifically include: the denoising intensity of the flat area is greater than the denoising intensity of the sub-flat area, greater than the denoising intensity of the sub-high frequency, greater than the denoising intensity of the high frequency, and the denoising intensity of the flat area is approximately equal to the denoising intensity of the flat area near the edge.

The technical scheme provided by the embodiment of the application has the advantages that the detection result accuracy is high, the frequency domain grade of the current pixel point to be processed can be effectively distinguished, the flat area near the edge can also be effectively distinguished, further, denoising can be carried out by using different intensities according to the frequency domain condition distinguished by refinement, and different denoising strategies are used for processing the flat area near the edge, the image data is enabled to keep the existence of detail information while the noise removal is ensured by using the method, and the frequency division method can greatly improve the effect of a related denoising algorithm; the embodiment of the application judges by setting the threshold value, so that the flexibility is higher; the embodiment of the application does not need operations such as wavelet decomposition and the like in the process of judging the frequency domain, and the calculation complexity is relatively low.

Referring to fig. 4, fig. 4 provides a terminal, which may include, as shown in fig. 4:

an obtaining unit 401, configured to obtain pixel data in _ data [ i ] [ j ] of a neighborhood n × n of a pixel point to be processed of the image signal with a current pixel point position to be processed as a center, where i =0,1, \ 8230;, n-1; j =0,1, \8230, if n-1, in _ data [ (n-1)/2 ] [ (n-1)/2 ] is the current pixel point to be detected;

a processing unit 402, configured to extract two template pixel data with different sizes from the pixel data of the neighborhood nxn, convert the extracted two template pixel data into two sets of luminance information, and calculate variances of the two sets of luminance information according to the two sets of luminance information, respectively, to obtain two variance values; judging the frequency domain characteristics of the current pixel point to be processed according to the relationship between the two variance values and a plurality of preset thresholds; and selecting corresponding strength for the relevant processing according to the judged frequency domain characteristic result.

The terminal provided by the application has high detection result accuracy, can effectively distinguish the frequency domain grade of the current pixel point to be processed, can effectively extract the flat area near the edge, further can perform denoising by using different intensities according to the frequency domain condition distinguished by refinement, and can process the flat area near the edge by using different denoising strategies, so that the image data can keep the existence of detail information while ensuring the noise removal by using the method, and the frequency division method can greatly improve the effect of a related denoising algorithm; the embodiment of the application judges by setting the threshold value, so that the flexibility is higher; the embodiment of the application does not need operations such as wavelet decomposition and the like in the process of judging the frequency domain, and the calculation complexity is relatively low.

In an alternative arrangement, the first and second electrodes may be,

the two different size templates may be a large size template and a small size template, where the large size template is n × n pixel data in a neighborhood, the small size template is m × m pixel data in the neighborhood, the m × m pixel data of the template is located in a central area of the n × n template pixel data, n and m are both odd numbers, and a correspondence between m and n is n =2 × m-3 and m ≧ 5.

In an alternative, the above

The processing unit 402 specifically uses the luminance information of the large-size template converted from the pixel data of the neighborhood large-size template nxn by means of gaussian filtering in a unit of 3 × 3;

the processing unit 402 specifically uses luminance information of the small-sized template into which the pixel data of the neighborhood small-sized template mxm is converted in a gaussian filtering manner in a unit of 3 × 3.

Gauss for converting brightness value of neighborhood pixel informationThe kernel function of the filtering is

wherein mean _ is the mean value of lum _ data _ [ i ] [ j ]:

wherein mean _ is the mean of lum _ data _ [ ] [ ]:

Taking the pixel size shown in fig. 2a as an example, the corresponding calculation relationship between the 11 × 11bayer data of the large-size template of the current pixel neighborhood to be detected and the converted luminance information lum _ data _ b is as follows:

wherein in _ is 11 × 11 neighborhood pixel data of a pixel point to be processed, namely large-size template pixel data of a neighborhood of the pixel point to be processed, lum _ data _ b [ i ] [ j ] is a corresponding brightness information value converted by input neighborhood large-size template bayer pixel data, i =0,1, \82304; j =0,1, \ 82304; 4.

Taking the pixel size shown in fig. 2a as an example, the corresponding calculation relationship between the small-size template 7 × 7bayer data of the current pixel neighborhood to be detected and the converted luminance information lum _ data _ s is as follows:

In an alternative arrangement, the first and second electrodes may be,

the plurality of thresholds includes: a small-size template-dependent division threshold sigma _ s and 3 large-size template-dependent division thresholds: sigma _0, sigma _1, and sigma _2, and sigma _0 is woven over sigma _1and sigma _2.

In an alternative, the processing unit 402 is specifically configured to

if var _ s is larger than or equal to sigma _ s and sigma _0 is larger than or equal to var _ b and smaller than sigma _1, determining the current pixel point to be processed as a sub-flat area;

In an alternative arrangement, the first and second electrodes may be,

the processing unit 402 is specifically configured to, when the frequency domain feature of the pixel to be processed is determined to be a flat region near the edge, select only pixels of the same channel of the small-size template neighborhood data of mxm to perform filtering and denoising in a weighted average manner; and when the frequency domain characteristics are judged to be the rest areas, filtering and denoising by using the same-channel pixel points of the n multiplied by n large-size template neighborhood data in a weighted average mode.

The setting of the filtering and denoising strength in the weighted average manner may specifically include:

when filtering, the denoising strength is correspondingly set according to the frequency division result, which specifically comprises: the denoising strength of the flat region is more than the denoising strength of the sub-flat region, more than the denoising strength of the sub-high frequency, more than the denoising strength of the high frequency, and the denoising strength of the flat region is approximately equal to the denoising strength of the flat region near the edge.

Referring to fig. 5, fig. 5 is a terminal 70 according to an embodiment of the present application, where the terminal 70 includes a processor 701, a memory 702, and a communication interface 703, and the processor 701, the memory 702, and the communication interface 703 are connected to each other through a bus 704.

The memory 702 includes, but is not limited to, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), or a portable read-only memory (CD-ROM), and the memory 702 is used for related computer programs and data. The communication interface 703 is used for receiving and transmitting data.

The processor 701 may be one or more Central Processing Units (CPUs), and in the case that the processor 701 is one CPU, the CPU may be a single-core CPU or a multi-core CPU.

The processor 701 in the terminal 70 is configured to read the computer program code stored in the memory 702 and perform the following operations:

taking the current pixel point position to be processed as the center, acquiring pixel data in _ data [ i ] [ j ] of a neighborhood n multiplied by n of the pixel point to be processed of the image signal, wherein i =0,1, \ 8230, n-1; j =0,1, \8230, n-1, then in _ data [ (n-1)/2 ] [ (n-1)/2 ] is the current pixel point to be detected;

The processor 701 in the terminal 70 is configured to read the computer program code stored in the memory 702, and further execute the refinement scheme of the embodiment shown in fig. 2 or fig. 3, which is not described herein again.

The embodiment of the present application further provides a chip system, where the chip system includes at least one processor, a memory and an interface circuit, where the memory, the transceiver and the at least one processor are interconnected by a line, and the at least one memory stores a computer program; when the computer program is executed by the processor, the method flows shown in fig. 2 and fig. 3 are realized.

Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program runs on a network device, the method flows shown in fig. 2 and fig. 3 are implemented.

The embodiments of the present application also provide a computer program product, where when the computer program product runs on a terminal, the method flows shown in fig. 2 and fig. 3 are implemented.

Embodiments of the present application also provide a terminal including a processor, a memory, a communication interface, and one or more programs, the one or more programs being stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps in the method of the embodiment shown in fig. 2 or fig. 3.

The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It will be appreciated that the electronic device, in order to carry out the functions described above, may comprise corresponding hardware structures and/or software templates for performing the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments provided herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiment of the present application, the electronic device may be divided into the functional units according to the method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit. It should be noted that, in the embodiment of the present application, the division of the unit is schematic, and is only one logic function division, and when the actual implementation is realized, another division manner may be provided.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no acts or templates referred to are necessarily required by the application.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solutions of the present application, which are essential or part of the technical solutions contributing to the prior art, or all or part of the technical solutions, may be embodied in the form of a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.

Those skilled in the art will appreciate that all or part of the steps of the methods of the above embodiments may be implemented by a program, which is stored in a computer-readable memory, the memory including: flash Memory disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.

The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. A method for determining a frequency domain feature of an image signal, comprising:

2. The method of claim 1,

the two size templates are: the image processing device comprises a large-size template and a small-size template, wherein the large-size template is n × n pixel data, the small-size template is m × m pixel data, the m × m pixel data are located in the center area of the n × n pixel data, both n and m are odd numbers, the corresponding relation between m and n is n =2 × m-3, and m is larger than or equal to 5.

3. The method of claim 2, wherein converting the neighborhood nxn pixel data into two sets of luminance information using two size templates comprises:

converting the pixel data of the neighborhood large-size template nxn into the brightness information corresponding to the pixel data of the large-size template in a Gaussian filtering mode by taking 3 x 3 as a unit;

converting pixel data of a neighborhood small-size template mxm into brightness information corresponding to the pixel data of the small-size template in a Gaussian filtering mode by taking 3 × 3 as a unit;

the kernel function of the Gaussian filter used to convert the neighborhood pixel information into luminance information is

4. The method of claim 1, wherein the calculating the variance of the two sets of luminance information to obtain two variance values according to the two sets of luminance information comprises:

wherein mean _ b is the mean value of lum _ data _ b [ i ] [ j ]:

wherein mean _ s is the mean of lum _ data _ s [ i ] [ j ]:

the lum _ data _ b [ i ] [ j ] is a group of luminance information converted by the large-size template pixel data, and the lum _ data _ s [ i ] [ j ] is a group of luminance information converted by the small-size template pixel data.

5. The method of claim 4,

the plurality of thresholds includes: small size template threshold sigma _ s and 3 large size template thresholds: sigma _0, sigma _1, and sigma _2, and sigma _0< sigma _1< sigma _2.

6. The method according to claim 5, wherein the determining the frequency domain characteristic of the current pixel to be processed according to the relationship between the two variance values and a plurality of preset thresholds specifically comprises:

7. The method of claim 6,

filtering and denoising according to the frequency domain characteristic judgment result of the current pixel point to be processed and the denoising strategy corresponding to the frequency domain characteristic, specifically comprising:

8. The method of claim 7,

the weighted average method performs filtering and denoising, and when filtering, denoising intensity is correspondingly set according to a frequency division result, specifically including: the denoising strength of the flat region > the denoising strength of the sub-high frequency > the high frequency denoising strength, and the denoising strength of the flat region is approximately equal to the denoising strength of the flat region near the edge.

9. A terminal, comprising:

the image processing device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring pixel data in _ data [ i ] [ j ] of a neighborhood n multiplied by n of a pixel point to be processed of an image signal by taking the current pixel point to be processed as a center, wherein i =0, 1.. And n-1; j =0, 1., n-1, then in _ data [ (n-1)/2 ] [ (n-1)/2 ] is the current pixel point to be detected;

the processing unit is used for extracting two pieces of template pixel data with different sizes from the pixel data of the neighborhood nxn to convert the pixel data into two pieces of brightness information, and calculating the variance of the two pieces of brightness information respectively according to the two pieces of brightness information to obtain two variance values; judging the frequency domain characteristics of the current pixel point to be processed according to the relationship between the two variance values and a plurality of preset thresholds; and selecting corresponding strength for the correlation processing according to the judged frequency domain characteristic result.

10. A terminal comprising a processor, memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-8.

11. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-8.