CN103986922A - Image processing method - Google Patents
Image processing method Download PDFInfo
- Publication number
- CN103986922A CN103986922A CN201310049795.0A CN201310049795A CN103986922A CN 103986922 A CN103986922 A CN 103986922A CN 201310049795 A CN201310049795 A CN 201310049795A CN 103986922 A CN103986922 A CN 103986922A
- Authority
- CN
- China
- Prior art keywords
- image
- noise
- pattern
- luminance picture
- produce
- 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.)
- Granted
Links
Landscapes
- Image Processing (AREA)
Abstract
The invention provides an image processing method which includes the following steps: obtaining a source image of a Bayer pattern color array; executing a first-order image processing on the source image to generate a first luminance and chrominance format image; executing a second-order image processing on the source image so as to generate a second luminance and chrominance format image; then generating a noise inhibition image after executing noise filtering processing on the first brightness and chrominance format image; and firstly carrying out weighting processing on a luminance image in the noise inhibition image and a luminance image in the second luminance and chrominance format image and then combining a chrominance image in the second luminance and chrominance format image so as to generate a processed image. The noise reduction degree of the noise inhibition image is higher than the noise reduction degree of the second luminance and chrominance format image.
Description
Technical field
The invention relates to a kind of image processing techniques, and relate to especially a kind of reproduced image and naturally feel the image processing method of (natural appearance reproduction).
Background technology
In the application of digital camera, the noise filtering of photo-sensitive cell or electronic signal is a very important ring, for the digital image of ISO (High ISO), is even more important.In the process of digital image noise filtering, tend to, by the detail textures of object itself and the gradually also filtering in the lump such as layer shadow variation, make the detail textures in digital image present block distribution, lose natural character.
In order to maintain the sense naturally of digital image, generally for the reprocessing of picture noise filtering, be mainly sharpness (sharpness) the strengthening algorithm that utilizes image, to strengthen the object edge in image, reach the effect of sharpening.For the side effect that causes losing natural character after noise filtering, general common processing mode is to adjust parameter in noise filtering algorithm to alleviate this effect, or in the time of noise filtering level and smooth district (smoothing area), texture area (texture area) and the sharpened edge (sharpness edge) of detected image, to give respectively different noise filtering parameters.
For the excessive blurred picture after noise filtering, although can strengthen algorithm by definition, object edge or texture area are carried out to sharpening, image detail sense is increased.But in noise filtering algorithm, according to the difference of image smoothing district, texture area and sharpened edge, the practice that gives respectively different noise filtering parameters can cause the otherness of regional in image to become large, because the noise filtering parameter of zones of different often has difference to a certain degree.Thus, visually can feel more not nature.And if adjust noise filtering parameter to weaken the otherness of regional, can form again noise cannot the clean problem of filtering.Therefore, how presenting image natural character real for the image after noise filtering is problem to be solved.
Summary of the invention
The invention provides a kind of image processing method, carry out reprocessing for the image after noise filtering, retain thus more image detail information and reproduced image and naturally feel.
A kind of image processing method of the present invention, comprises the following steps.Obtain the source images that Bel figure (Bayer pattern) color is arranged.Source images is carried out to the first rank image and process to produce the first YC format-pattern.And source images is carried out to second-order image and process to produce the second YC format-pattern.Then, the first YC format-pattern is carried out after noise filtering (denoise) is processed and produced noise suppressed image.First the luminance picture in (luminance) image of the brightness in noise suppressed image and the second YC format-pattern is carried out after weight processing, colourity (chrominance) image merging in the second YC format-pattern is processed rear image to produce.Wherein the noise noise reduction degree of noise suppressed image is higher than the noise noise reduction degree of the second YC format-pattern.
In one embodiment of this invention, the step of above-mentioned execution the first rank image processing comprises first to be carried out after Bel's noise filtering (Bayer denoise) processing source images, carry out again color interpolation (color interpolation) processing and colour reconstruction (color reproduction) and process, to produce the first YC format-pattern.
In one embodiment of this invention, the step of above-mentioned execution second-order image processing comprises that directly source images being carried out to color interpolation processes and colour reconstruction processing, to produce the second YC format-pattern.
In one embodiment of this invention, above-mentioned after source images execution second-order image is processed to produce the step of the second YC format-pattern, also comprise the second YC format-pattern is changed, to isolate luminance picture and chromatic diagram picture.Then, produce corresponding noise pattern (noise map) for luminance picture.And, luminance picture, noise pattern and chromatic diagram are looked like to mix (blend), to produce the output image of tool different qualities noise.
In one embodiment of this invention, the picture size of above-mentioned noise pattern is same as the picture size of luminance picture.
In one embodiment of this invention, above-mentioned noise pattern comprises the numerical value of most positive signs or negative sign, and each numerical value corresponds to respectively each pixel in luminance picture.
In one embodiment of this invention, the step that wherein produces corresponding noise pattern for luminance picture comprises: each pixel in luminance picture is determined to NF scope (noise range) and noise offset amount separately (noise offset) separately, produce thus single-point noise figure (single pixel noise map); Each pixel in luminance picture is determined to T_BlurMasked (blur mask) separately, to produce image mask indicator diagram; And according to image mask indicator diagram, corresponding single-point noise figure is carried out to Fuzzy processing, to produce above-mentioned noise pattern.
In one embodiment of this invention, wherein first respectively each pixel in luminance picture is determined to each NF scope and each noise offset amount, the step that produces thus above-mentioned single-point noise figure comprises: block characteristic and brightness number according to luminance picture determine NF scope; Within the scope of NF, random number produces NF; Brightness number according to luminance picture is tabled look-up, to obtain noise offset amount; And respectively each NF of each pixel is added to each noise offset amount, to obtain above-mentioned single-point noise figure.
In one embodiment of this invention, wherein each pixel in luminance picture is determined to each T_BlurMasked, comprise with the step that produces above-mentioned image mask indicator diagram: according to the block characteristic of luminance picture and the brightness number of each pixel, in a T_BlurMasked database, choose respectively corresponding T_BlurMasked set; And for each pixel respectively in each T_BlurMasked set random number choose a T_BlurMasked, to form above-mentioned image mask indicator diagram.
In one embodiment of this invention, the T_BlurMasked of most tool different sizes of storage and different patterns in above-mentioned T_BlurMasked database.
Based on above-mentioned, image processing method provided by the present invention is exportable comprise image detail information and reach the processing of filtering noise effect after image, and the method can be avoided the blocky effect and the discontinuous sense of removal of images that produce after general noise filtering, so that image tool natural character after processing.
For above-mentioned feature and advantage of the present invention can be become apparent, special embodiment below, and coordinate accompanying drawing to be described in detail below.
Brief description of the drawings
Fig. 1 is the calcspar according to the image processing apparatus shown in first embodiment of the invention;
Fig. 2 is the flow chart according to the image processing method shown in first embodiment of the invention;
Fig. 3 is the calcspar according to the image processing apparatus shown in second embodiment of the invention;
Fig. 4 is the flow chart according to the image processing method shown in second embodiment of the invention;
Fig. 5 is the detailed execution mode of one according to the noise generation module shown in second embodiment of the invention;
Fig. 6 is according to producing the detailed execution mode of one of corresponding noise pattern for luminance picture shown in second embodiment of the invention.
Description of reference numerals:
100,300: image processing apparatus;
110: image collection module;
120: the first noise filtering modules;
130: colour reconstruction module;
140: the second noise filtering modules;
150,330: merge module;
200,400: method flow;
310: separation of images module;
320: noise generation module;
510: single-point noise determining means;
512: tandom number generator;
514: blender;
520: T_BlurMasked determining means;
530: Fuzzy Processing unit;
Img1～Img9: image;
S210～S250: each step of image processing method;
S410～S440: each step of image processing method.
Embodiment
The first embodiment
In the present embodiment, image after noise filtering has not sense naturally, by source images being separated into two different images handling processes and then producing two result images, wherein an image comprises more noise and image detail information, another image is comparatively fuzzy, finally two result images are made to weighting sum total, to produce image after the processing that there is noise filtering effect and comprise more image detail information.
Fig. 1 is the calcspar according to the image processing apparatus shown in first embodiment of the invention.Image processing apparatus 100 is for example the electronic installations such as digital camera, slr camera, Digital Video or other smart mobile phone with image processing function, panel computer, notebook computer, desktop computer, is not limited to above-mentioned.
Please refer to Fig. 1, image processing apparatus 100 comprises image collection module 110, the first noise filtering module 120, colour reconstruction module 130, the second noise filtering module 140 and merges module 150.Wherein, image collection module 110 comprises the member such as camera lens, photo-sensitive cell, in order to obtain image.Colour reconstruction module 130, first and second noise filtering module 120,140 and merge module 150 and can be the functional module that hardware and/or software are realized.Wherein hardware can comprise that central processing unit, chipset, microprocessor etc. have the combination of hardware device or the above-mentioned hardware device of image operation processing capacity, and software can be operating system, driver etc.
Fig. 2 is the flow chart according to the image processing method shown in first embodiment of the invention.Please refer to Fig. 2, the method 200 of the present embodiment is applicable to image processing apparatus 100, below the arrange in pairs or groups detailed step of the each module declaration the present embodiment in image processing apparatus 100:
First, in step S210, the source images Img1 that image collection module 110 is arranged in order to obtain Bel figure (Bayer pattern) color.
Then in step S220, source images Img1 is carried out to the first rank image and process to produce the first YC format-pattern (be YCbCr format-pattern, be designated hereinafter simply as a YCbCr image I mg2).Wherein, step S120 also can be divided into sub-step S222 and S224.That is to say, the step of the first rank image processing comprises first utilizes the first noise filtering module 120 to come source images Img1 to carry out Bel's noise filtering processing (step S122).Afterwards, colour reconstruction module 130 is carried out color interpolation again and is processed and colour reconstruction processing, to produce a YCbCr image I mg2.
Specifically, because each pixel that Bel's graph coloring is had a dress rehearsal in the source images Img1 of row only has wherein a kind of color of red channel (R channel), green channel (G channel) or blue channel (B channel), not general rgb format image or the YCbCr format-pattern that shows use.Therefore, colour reconstruction module 130 can be done color interpolation and process to produce the three primary colors image of general demonstration use.Further, for color correctly presents, colour reconstruction module 130 also can be carried out the color reconstruction process such as black compensation (black offset), RGB gain (RGB gain) adjustment, colour correction (Color correction), Gamma correction (Gamma correction).Afterwards, a YCbCr image I mg2 is changed and exported to colour reconstruction module 130.
Then in step S230, the second noise filtering module 140 is also carried out noise filtering to a YCbCr image I mg2, produces comparatively fuzzy noise suppressed image I mg3.Object details in this noise suppressed image I mg3 or texture are conventionally processed and disappear along with noise filtering, and it is unnatural that human eye can be felt conventionally.
On the other hand, as described in step S240, colour reconstruction module 130 is carried out second-order image to source images Img1 again and is processed to produce the 2nd YCbCr image I mg4.The step of carrying out the processing of second-order image comprises that directly source images Img1 being carried out to color interpolation by colour reconstruction module 130 processes and colour reconstruction processing, to produce details the 2nd YCbCr image I mg4 comparatively clearly.Wherein, colour reconstruction module 130 is carried out part processing (for example carrying out colour correction) can make noise profile change, and produces factitious noise sense (for example the noise of some color zone of saturation is higher).Therefore, colour reconstruction module 130 can be adjusted the corresponding parameter of colour correction, processes compared to the first rank image, and correction intensity weakens many, so that the image brightness noise of the 2nd YCbCr image I mg4 keeps naturally sensation.In other words, the noise noise reduction degree of noise suppressed image I mg3 is higher than the noise noise reduction degree of the 2nd YCbCr image I mg4.
At step S250, merge module 150 and first brightness (luminance) image in fuzzyyer noise suppressed image I mg3 and the luminance picture in the 2nd YCbCr image I mg4 that contains more noise and details are carried out to weight processing (also claiming weighted sum (weighting sum) computing).Finally, merge module 150 and remerge colourity (chrominance) image in the 2nd YCbCr image to produce image I mg5 after the processing that comprises more object detailed information.Should be noted that, because this weight processing also can be mixed to noise locating in inner rear image I mg5 of output in the lump, these noises are naturally not feeling of block shape after can alleviating some noise filterings, make image more natural.
Due to the impression degree difference of human eye to light and shade noise, when therefore merging module 150 and being in the end weighted with computing, the brightness value that can reference noise suppresses image I mg3 decides weight, with produce human eye impression preferably process after image I mg5.For instance, for the pixel that in noise suppressed image I mg3, brightness value is 100, the weight that merges module 150 mixed noise suppression image I mg3 and the 2nd YCbCr image I mg4 is for example set as 80: 20; Because human eye tends to comparatively sensitivity to dark portion noise, therefore to the pixel that in noise suppressed image I mg3, brightness value is 10, the weight that merges module 150 mixed noise suppression image I mg3 and the 2nd YCbCr image I mg4 is for example set as 90: 10.In one embodiment, the weight setting for different brightness values for example can conventionally know that the knowledgeable is beforehand with by this area tool and set and be stored as form so that merge module 150 can be in memory cell (not illustrating at Fig. 1) weight setting value corresponding to quick search.
In the present embodiment, owing to having many same or similar treatment steps in the first rank image handling process and second-order image handling process, therefore can adopt same hardware (being for example colour reconstruction module 130) but the mode of computing repeatedly, to reduce the cost in hardware designs.The present embodiment not only can allow image after noise filtering retain image detail information, and the discontinuous sense of image quality that also can removal of images promotes naturally feeling and image quality of image after processing.
The second embodiment
In the present embodiment, after noise filtering, cause naturally not feeling of image, also can be by produce at random many noise spots in image, and the noise spot of these tool different qualities is blended in the image after noise filtering to the technology of reappearing to reach nature sense.
Fig. 3 is the calcspar according to the image processing apparatus shown in second embodiment of the invention.Image processing apparatus 300 is for example the electronic installations such as digital camera, slr camera, Digital Video or other smart mobile phone with image processing function, panel computer, notebook computer, desktop computer, is not limited to above-mentioned.
Please refer to Fig. 3, image processing apparatus 300 comprises separation of images module 310, noise generation module 320 and merges module 330.Separation of images module 310 is in order to be divided into received input picture on luminance picture and chromatic diagram picture.Noise generation module 320 is in order to produce noise pattern (noise map).Merge module 330 luminance picture, noise pattern and chromatic diagram are looked like to mix (blend), to produce the output image of tool different qualities noise.Above-mentioned each module can be the functional module that hardware and/or software are realized.Wherein hardware can comprise that central processing unit, chipset, microprocessor etc. have the combination of hardware device or the above-mentioned hardware device of image operation processing capacity, and software can be operating system, driver etc.
Fig. 4 is the flow chart according to the image processing method shown in second embodiment of the invention.Please refer to Fig. 4, the method 400 of the present embodiment is applicable to image processing apparatus 300, below the arrange in pairs or groups detailed step of the each module declaration the present embodiment in image processing apparatus 300:
Step S410, separation of images module 310 receives an input picture Img6.Wherein, this input picture Img6 is for example the 2nd YCbCr image I mg4 in the first embodiment.Next, at step S420, separation of images module 310 is changed this input picture Img6, isolates luminance picture (, Y channel image) Img7 and chromatic diagram picture (, CbCr channel image) Img8.Wherein, luminance picture Img7 is sent to respectively noise generation module 320 and merges module 330; Chromatic diagram is directly sent to and merges module 330 as Img8.
At step S430, noise generation module 320 produces corresponding noise pattern Img9 for luminance picture Img7.Wherein, the picture size of noise pattern Img9 (size) is same as the picture size of luminance picture Img7.Noise pattern Img9 is the numerical value that comprises most positive signs or negative sign, and wherein each numerical value corresponds to respectively each pixel in luminance picture Img7.
Finally, at step S440, merge module 330 luminance picture Img7, noise pattern Img9 and chromatic diagram are mixed as Img8, to produce the output image Img10 of tool different qualities noise.In detail, merge module 330 and first the brightness value of each pixel in luminance picture Img7 is added with NF corresponding in noise pattern Img9 respectively, to produce the luminance picture that comprises noise spot.Then, merge module 330 and again the luminance picture that comprises noise spot is mixed as Img8 with chromatic diagram, to produce the output image Img10 with nature sense.Wherein, this input picture Img10 for example can be used as the image of input merging module 150 in the first embodiment.
Should be noted that, in luminance picture Img7, add noise spot to be equal to the function of offseting noise filtering, therefore these noise spots not can produce arbitrarily at random, must meet user's natural viewpoint.That is to say must be with reference to the hobby of human eye vision, and the present embodiment is the noise spot that adds different size, varying strength and different trend according to the difference of image brightness, allows user have better visual experience.Below how the noise generation module 320 that describes the present embodiment with Fig. 5 and Fig. 6 in detail is produced to corresponding noise pattern Img9 for luminance picture Img7.
Fig. 5 is the detailed execution mode of one according to the noise generation module 320 shown in second embodiment of the invention.Please refer to Fig. 5, noise generation module 320 comprises single-point noise determining means 510, T_BlurMasked (blur mask) determining means 520 and Fuzzy Processing unit 530.Wherein, single-point noise determining means 510 also comprises tandom number generator 512 and blender (mixer) 514.
Fig. 6 is according to producing the detailed execution mode of one of corresponding noise pattern for luminance picture shown in second embodiment of the invention.Below please coordinate with reference to Fig. 5 and Fig. 6 simultaneously.
First,, at step S610, noise generation module 320 receives a luminance picture Img7.Luminance picture Img7 is sent to respectively single-point noise determining means 510 and T_BlurMasked determining means 520 is processed.
At step S620, single-point noise determining means 510 first determines NF scope (noise range) and noise offset amount separately (noise offset) separately to each pixel in luminance picture, produces thus single-point noise figure (single pixel noise map).In detail, single-point noise determining means 510 first decides NF scope (TH according to the block characteristic of luminance picture Img7 and the brightness number of each pixel
range, TH
range), wherein TH
rangefor being greater than 0 positive number.Then, produce a NF by tandom number generator 512 random numbers, wherein this NF drops on NF scope (TH
range, TH
range) between.Next, blender 514 adds this NF a corresponding noise offset amount again, in order to control noise mean intensity.Each pixel in luminance picture Img7 all, after above-mentioned processing, just can obtain single-point noise figure (single pixel noise map).Wherein, noise offset amount can know that the knowledgeable does setting in advance with the form of form conventionally by this area tool, tables look-up according to the brightness number of luminance picture Img7, just can obtain noise offset amount.The function of noise offset amount also comprises enhancing picture contrast, so that brighter, the dark portion of image highlights is darker.
On the other hand, at step S630, T_BlurMasked determining means 520 determines T_BlurMasked (blur mask) separately to each pixel in luminance picture, to produce image mask indicator diagram.In detail, can be different due to the numerical value change of each noise in single-point noise figure and the apparent distribution of natural noise, natural noise may clustering form circle or the distribution of other shapes.Therefore, also need single-point noise figure to use image mask to produce the noise of different-style, so-called image mask is T_BlurMasked.
Because noise in image style is along with brightness is slightly different, and same brightness also has multiple noise style.Therefore, T_BlurMasked determining means 520 can, first according to the block characteristic of luminance picture and the brightness number of each pixel, be chosen corresponding T_BlurMasked set in a T_BlurMasked database.The T_BlurMasked of most tool different sizes of storage and different patterns in T_BlurMasked database.For instance, T_BlurMasked can be the N*N shade of standard, and N is greater than 0 positive integer.In T_BlurMasked database, can give each T_BlurMasked a numbering, supposing has 10 groups of T_BlurMaskeds in T_BlurMasked database, is numbered respectively 1～10.
For instance, table 1 is according to a kind of brightness shown in the second embodiment and T_BlurMasked set table.Please refer to following table 1, is 0 for example can use the T_BlurMasked that is numbered 1,2 or 5 corresponding to brightness number; Be 1 for example can use the T_BlurMasked that is numbered 2,3 or 4 corresponding to brightness number; The like.
Table 1
| Brightness number | T_BlurMasked set |
| 0 | 1、2、5 |
| 1 | 2、3、4 |
| 2 | 1、3 |
| ... | ... |
| 255 | 10 |
T_BlurMasked determining means 520 is tabled look-up according to brightness value again, selects a T_BlurMasked of corresponding this pixel, and record its numbering with random number from corresponding T_BlurMasked set.After recording its corresponding T_BlurMasked numbering for each pixel, produce the image mask indicator diagram of the present embodiment.
Get back to Fig. 6, at step S640, Fuzzy Processing unit 530 carries out Fuzzy processing according to image mask indicator diagram by corresponding single-point noise figure, just can produce the noise pattern Img9 of tool different qualities.
In sum, the present invention adopts dual image to process the mode in path, and the first rank image is treated to the blurred picture producing after noise filtering, and second-order image is treated to the picture rich in detail that retains image detail information and/or tool different qualities noise.By considering that two images do weighted blend by image bright dark degree, the image containing image detail information and after reaching the processing of filtering noise effect with output packet.In addition, the image processing method that the present invention adopts can be avoided the blocky effect and the discontinuous sense of removal of images that after general noise filtering, produce, so that image tool natural character after processing.
Finally it should be noted that: above each embodiment, only in order to technical scheme of the present invention to be described, is not intended to limit; Although the present invention is had been described in detail with reference to aforementioned each embodiment, those of ordinary skill in the art is to be understood that: its technical scheme that still can record aforementioned each embodiment is modified, or some or all of technical characterictic is wherein equal to replacement; And these amendments or replacement do not make the essence of appropriate technical solution depart from the scope of various embodiments of the present invention technical scheme.
Claims (10)
1. an image processing method, is characterized in that, comprising:
Receive a source images;
This source images is carried out to one first rank image and process to produce one first YC format-pattern;
This source images is carried out to a second-order image and process to produce one second YC format-pattern;
This first YC format-pattern is carried out after a noise suppressed is processed and produced a noise suppressed image; And
First the luminance picture in the luminance picture in this noise suppressed image and this second YC format-pattern is carried out after weight processing, merge chromatic diagram picture in this second YC format-pattern to produce image after a processing, wherein the noise noise reduction degree of this noise suppressed image is higher than the noise noise reduction degree of this second YC format-pattern.
2. image processing method according to claim 1, is characterized in that, the step of carrying out this first rank image processing comprises:
This source images is carried out after Bel's noise filtering processing, then carried out a color interpolation and process and a color reconstruction process, to produce this first YC format-pattern.
3. image processing method according to claim 2, is characterized in that, the step of carrying out this second-order image processing comprises:
Directly this source images is carried out to this color interpolation and process and this colour reconstruction processing, to produce this second YC format-pattern.
4. image processing method according to claim 1, is characterized in that, this source images being carried out after this second-order image processes to produce the step of this second YC format-pattern, also comprises:
This second YC format-pattern is changed, to isolate a luminance picture and a colourity image;
Produce a corresponding noise pattern for this luminance picture; And
This luminance picture, noise pattern are looked like to mix with this chromatic diagram, to produce an output image of tool different qualities noise.
5. image processing method according to claim 4, is characterized in that, the picture size of this noise pattern is same as the picture size of this luminance picture.
6. image processing method according to claim 4, is characterized in that, this noise pattern comprises the numerical value of most positive signs or negative sign, and each numerical value corresponds to respectively each pixel in this luminance picture.
7. image processing method according to claim 4, is characterized in that, the step that produces this corresponding noise pattern for this luminance picture comprises:
Each pixel in this luminance picture is determined to a NF scope separately and a noise offset amount separately, produce thus a single-point noise figure;
Each pixel in this luminance picture is determined to a T_BlurMasked separately, to produce an image mask indicator diagram; And
According to this image mask indicator diagram, this single-point noise figure of correspondence is carried out to Fuzzy processing, to produce this noise pattern.
8. image processing method according to claim 7, is characterized in that, first respectively respectively this pixel in this luminance picture is determined to respectively this NF scope and respectively this noise offset amount, and the step that produces thus this single-point noise figure comprises:
A block characteristic and a brightness number according to this luminance picture determine this NF scope;
Within the scope of this NF, random number produces a NF;
This brightness number according to this luminance picture is tabled look-up, to obtain this noise offset amount; And
Respectively each respectively this NF of this pixel is added to respectively this noise offset amount, to obtain this single-point noise figure.
9. image processing method according to claim 7, is characterized in that, respectively this pixel in this luminance picture is determined to respectively this T_BlurMasked, comprises with the step that produces this image mask indicator diagram:
According to a block characteristic of this luminance picture and an each brightness number of this pixel, in a T_BlurMasked database, choose respectively a corresponding T_BlurMasked set; And
For each this pixel respectively in each this T_BlurMasked set random number choose a T_BlurMasked, to form this image mask indicator diagram.
10. image processing method according to claim 9, is characterized in that, the T_BlurMasked of most tool different sizes of storage and different patterns in this T_BlurMasked database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310049795.0A CN103986922B (en) | 2013-02-07 | 2013-02-07 | Image processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310049795.0A CN103986922B (en) | 2013-02-07 | 2013-02-07 | Image processing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103986922A true CN103986922A (en) | 2014-08-13 |
| CN103986922B CN103986922B (en) | 2016-03-02 |
Family
ID=51278736
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310049795.0A Active CN103986922B (en) | 2013-02-07 | 2013-02-07 | Image processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103986922B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106531125A (en) * | 2017-01-04 | 2017-03-22 | 深圳创维－Rgb电子有限公司 | Adjustment method and device of image display brightness and TV set |
| CN111915528A (en) * | 2020-08-05 | 2020-11-10 | 广州市百果园信息技术有限公司 | Image brightening method and device, mobile terminal and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040091174A1 (en) * | 2003-10-30 | 2004-05-13 | Samsung Electronics Co., Ltd. | Method and apparatus for image interpolation based on adaptive polyphase filters |
| US20050074224A1 (en) * | 2002-04-11 | 2005-04-07 | Fuji Photo Film Co., Ltd. | Apparatus for modifying reproduction frequency band of video signal |
| CN1744702A (en) * | 2004-09-03 | 2006-03-08 | 乐金电子(中国)研究开发中心有限公司 | Image sharpness regulation device of mobile terminal and method thereof |
| CN101188773A (en) * | 2006-11-24 | 2008-05-28 | 奥林巴斯映像株式会社 | Camera and image processing method |
| WO2010101292A1 (en) * | 2009-03-03 | 2010-09-10 | Sharp Kabushiki Kaisha | Method of and apparatus for processing a video image |
| CN102222326A (en) * | 2011-06-28 | 2011-10-19 | 青岛海信信芯科技有限公司 | Method and device for deblurring images based on single low resolution |
-
2013
- 2013-02-07 CN CN201310049795.0A patent/CN103986922B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050074224A1 (en) * | 2002-04-11 | 2005-04-07 | Fuji Photo Film Co., Ltd. | Apparatus for modifying reproduction frequency band of video signal |
| US20040091174A1 (en) * | 2003-10-30 | 2004-05-13 | Samsung Electronics Co., Ltd. | Method and apparatus for image interpolation based on adaptive polyphase filters |
| CN1744702A (en) * | 2004-09-03 | 2006-03-08 | 乐金电子(中国)研究开发中心有限公司 | Image sharpness regulation device of mobile terminal and method thereof |
| CN101188773A (en) * | 2006-11-24 | 2008-05-28 | 奥林巴斯映像株式会社 | Camera and image processing method |
| WO2010101292A1 (en) * | 2009-03-03 | 2010-09-10 | Sharp Kabushiki Kaisha | Method of and apparatus for processing a video image |
| CN102222326A (en) * | 2011-06-28 | 2011-10-19 | 青岛海信信芯科技有限公司 | Method and device for deblurring images based on single low resolution |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106531125A (en) * | 2017-01-04 | 2017-03-22 | 深圳创维－Rgb电子有限公司 | Adjustment method and device of image display brightness and TV set |
| CN106531125B (en) * | 2017-01-04 | 2019-02-01 | 深圳创维－Rgb电子有限公司 | Method of adjustment, device and the television set of image display brightness |
| CN111915528A (en) * | 2020-08-05 | 2020-11-10 | 广州市百果园信息技术有限公司 | Image brightening method and device, mobile terminal and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103986922B (en) | 2016-03-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| TWI511559B (en) | Image processing method | |
| CN105574918B (en) | A kind of material adding method, device and the terminal of 3D models | |
| Ancuti et al. | Enhancing by saliency-guided decolorization | |
| CA2462486C (en) | Automatic color correction for sequences of images | |
| US6262778B1 (en) | Image processing system | |
| JPH04234261A (en) | Method of projecting color image onto monochrome image | |
| CN104935902B (en) | Image color Enhancement Method, device and electronic equipment | |
| JP2000134486A (en) | Image processing unit, image processing method and storage medium | |
| CN111064942B (en) | Image processing method and apparatus | |
| CN113507598B (en) | Video picture display method, device, terminal and storage medium | |
| CN110288670A (en) | A kind of UI retouches the high-performance rendering method of side special efficacy | |
| Zamir et al. | Vision models for wide color gamut imaging in cinema | |
| CN107592517B (en) | Skin color processing method and device | |
| JP2006332908A (en) | Color image display apparatus, color image display method, program, and recording medium | |
| US7664322B1 (en) | Feature-based color adjustment | |
| US20070085857A1 (en) | Method and apparatus for adaptively filtering input image in color domains | |
| US7071947B1 (en) | Automatic adjustment of floating point output images | |
| US20100284614A1 (en) | Blue sky color detection technique | |
| CN103986922B (en) | Image processing method | |
| US8922580B2 (en) | Method and system to modify a color lookup table | |
| JP2003304400A (en) | Image processing apparatus and method, program, and recording medium | |
| CN111338627B (en) | Front-end webpage theme color adjustment method and device | |
| CN114820822A (en) | Image processing method and device, electronic equipment and readable storage medium | |
| Kuang et al. | iCAM06, HDR, and image appearance | |
| Zamir et al. | Perceptually-based gamut extension algorithm for emerging wide color gamut display and projection technologies |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |