CN103986922B - Image processing method - Google Patents
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- CN103986922B CN103986922B CN201310049795.0A CN201310049795A CN103986922B CN 103986922 B CN103986922 B CN 103986922B CN 201310049795 A CN201310049795 A CN 201310049795A CN 103986922 B CN103986922 B CN 103986922B
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Abstract
The invention provides a kind of image processing method, comprise the following steps.Obtain the source images of Bel's graph coloring dress rehearsal row.First rank image procossing is performed to produce the first YC format-pattern to source images.And second-order image procossing is performed to produce the second YC format-pattern to source images.Then, noise suppressed image is produced after noise filtering process being performed to the first YC format-pattern.After first carrying out weight process to the luminance picture in the luminance picture in noise suppressed image and the second YC format-pattern, the chromatic diagram picture merged in the second YC format-pattern processes rear image to produce.The noise noise reduction degree of noise suppressed image is higher than the noise noise reduction degree of the second YC format-pattern.
Description
Technical field
The invention relates to a kind of image processing techniques, and relate to a kind of image processing method of reproduced image natural sense (naturalappearancereproduction) especially.
Background technology
In the application of digital camera, the noise filtering of photo-sensitive cell or electronic signal is a very important ring, for ISO (HighISO) digital image be even more important.In the process of digital image noise filtering, often by the detail textures of object itself and the gradually also filtering in the lump such as layer shadow change, make the detail textures in digital image present block distribution, lose natural character.
In order to maintain the natural sense of digital image, generally for the reprocessing of picture noise filtering, mainly utilizing the sharpness of image (sharpness) to strengthen algorithm, to strengthen the object edge in image, reaching the effect of sharpening.For causing the side effect losing natural character after noise filtering, generally common processing mode is that parameter in adjustment noise filtering algorithm is to alleviate this effect, or when noise filtering the smooth area (smoothingarea) of detected image, texture area (texturearea) and sharpened edge (sharpnessedge), to give different noise filtering parameters respectively.
For the excessive blurred picture after noise filtering, although definition can be used to strengthen algorithm object edge or texture area are carried out 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 giving different noise filtering parameters respectively 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, more not nature can visually be felt.And if adjustment 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 more image detail information thus and reproduced image natural sense.
A kind of image processing method of the present invention, comprises the following steps.Obtain the source images of Bel figure (Bayerpattern) color arrangement.First rank image procossing is performed to produce the first YC format-pattern to source images.And second-order image procossing is performed to produce the second YC format-pattern to source images.Then, noise suppressed image is produced after noise filtering (denoise) process being performed to the first YC format-pattern.After first carrying out weight process to brightness (luminance) image in noise suppressed image and the luminance picture in the second YC format-pattern, colourity (chrominance) image merged in the second YC format-pattern processes 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 first rank image procossing comprises first to after source images execution Bel's noise filtering (Bayerdenoise) process, perform color interpolation (colorinterpolation) process and colour reconstruction (colorreproduction) process again, to produce the first YC format-pattern.
In one embodiment of this invention, the step of above-mentioned execution second-order image procossing comprises directly to source images execution color interpolation process and colour reconstruction process, to produce the second YC format-pattern.
In one embodiment of this invention, above-mentioned source images is performed second-order image procossing with the step producing the second YC format-pattern after, also comprise and the second YC format-pattern changed, to isolate luminance picture and chromatic diagram picture.Then, corresponding noise pattern (noisemap) is produced for luminance picture.Further, luminance picture, noise pattern and chromatic diagram picture are carried out mixing (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 each pixel in luminance picture respectively.
In one embodiment of this invention, the step wherein producing the noise pattern of correspondence for luminance picture comprises: determine respective NF scope (noiserange) and respective noise offset amount (noiseoffset) to each pixel in luminance picture, produces single-point noise figure (singlepixelnoisemap) thus; Respective T_BlurMasked (blurmask) is determined, to produce image mask indicator diagram to each pixel in luminance picture; And the single-point noise figure of correspondence is carried out Fuzzy processing, to produce above-mentioned noise pattern according to image mask indicator diagram.
In one embodiment of this invention, wherein first determine each NF scope and each noise offset amount to each pixel in luminance picture respectively, the step producing above-mentioned single-point noise figure thus comprises: according to patch characteristics and the brightness number decision NF scope of luminance picture; 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 each noise offset amount, to obtain above-mentioned single-point noise figure.
In one embodiment of this invention, wherein each T_BlurMasked is determined to each pixel in luminance picture, comprise with the step producing above-mentioned image mask indicator diagram: according to the patch characteristics of luminance picture and the brightness number of each pixel, in a T_BlurMasked database, choose corresponding T_BlurMasked set respectively; 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 and different pattern is stored in above-mentioned T_BlurMasked database.
Based on above-mentioned, image processing method provided by the present invention is exportable comprises image detail information and image after reaching the process of filtering noise effect, and the blocky effect that produces after can avoiding general noise filtering of the method and the discontinuous sense of removal of images, to make image tool natural character after process.
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.
Accompanying drawing explanation
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 a kind of detailed embodiment according to the noise generation module shown in second embodiment of the invention;
Fig. 6 is according to a kind of detailed embodiment producing 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
First embodiment
In the present embodiment, in order to avoid the image after noise filtering has not natural sense, 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 then comparatively fuzzy, finally two result images are made weight summation, there is noise filtering effect and image after comprising the process of more image detail information to produce.
Fig. 1 is the calcspar according to the image processing apparatus shown in first embodiment of the invention.Image processing apparatus 100 is such as digital camera, slr camera, Digital Video or other have the electronic installation such as smart mobile phone, panel computer, notebook computer, desktop computer of image processing function, is not limited to above-mentioned.
Please refer to Fig. 1, image processing apparatus 100 comprises image collection module 110, first noise filtering module 120, colour reconstruction module 130, second noise filtering module 140 and merges module 150.Wherein, image collection module 110 comprises the component 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 realizes.Wherein hardware can comprise central processing unit, chipset, microprocessor etc. and has the hardware device of image operation processing capacity or the combination of above-mentioned hardware device, and software can be then 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, the detailed step of each module declaration the present embodiment in image processing apparatus 100 of namely arranging in pairs or groups below:
First, in step S210, image collection module 110 is in order to obtain the source images Img1 of Bel figure (Bayerpattern) color arrangement.
Then in step S220, the first rank image procossing is performed to produce the first YC format-pattern (i.e. YCbCr format-pattern, hereinafter referred to as a YCbCr image Img2) to source images Img1.Wherein, step S120 also can be divided into sub-step S222 and S224.That is, the step of the first rank image procossing comprise first utilize the first noise filtering module 120 come to source images Img1 perform Bel's noise filtering process (step S122).Afterwards, colour reconstruction module 130 performs color interpolation process and colour reconstruction process again, to produce a YCbCr image Img2.
Specifically, because Bel's graph coloring each pixel of having a dress rehearsal in the source images Img1 of row only has wherein a kind of color of red channel (Rchannel), green channel (Gchannel) or blue channel (Bchannel), the rgb format image of not general display or YCbCr format-pattern.Therefore, colour reconstruction module 130 can do color interpolation process to produce the three-primary-color image of general display.Further, in order to color correctly presents, colour reconstruction module 130 also can carry out the color reconstruction process such as black compensation (blackoffset), RGB gain (RGBgain) adjustment, colour correction (Colorcorrection), Gamma correction (Gammacorrection).Afterwards, colour reconstruction module 130 is changed and is exported a YCbCr image Img2.
Then in step S230, the second noise filtering module 140 also carries out noise filtering to a YCbCr image Img2, produces comparatively fuzzy noise suppressed image Img3.Object detail in this noise suppressed image Img3 or texture disappear along with noise filtering process usually, and human eye can feel not nature usually.
On the other hand, as described in step S240, colour reconstruction module 130 performs second-order image procossing to produce the 2nd YCbCr image Img4 to source images Img1 again.The step performing second-order image procossing comprises by colour reconstruction module 130 directly to source images Img1 execution color interpolation process and colour reconstruction process, to produce details the 2nd YCbCr image Img4 comparatively clearly.Wherein, colour reconstruction module 130 carries out part process (such as carrying out colour correction) can make noise profile change, and produces factitious noise sense (such as the noise of some color zone of saturation is higher).Therefore, colour reconstruction module 130 can adjust the corresponding parameter of colour correction, and compared to the first rank image procossing, correction intensity weakens many, keeps naturally sensation to make the image brightness noise of the 2nd YCbCr image Img4.In other words, the noise noise reduction degree of noise suppressed image Img3 is higher than the noise noise reduction degree of the 2nd YCbCr image Img4.
In step S250, merge module 150 and first brightness (luminance) image in fuzzyyer noise suppressed image Img3 and the luminance picture in the 2nd YCbCr image Img4 containing more noise and details are carried out weight process (also claiming, weighted sum (weightingsum) computing).Finally, merge module 150 to remerge colourity (chrominance) image in the 2nd YCbCr image and comprise image Img5 after the process of more object detail information to produce.Should be noted that, due in image Img5 after in the place that noise also can be mixed to output by this weight process in the lump, these noises can alleviate the not natural sense in block shape after some noise filterings, make image more natural.
Because human eye is different to the impression degree of light and shade noise, when therefore merging module 150 is in the end weighted with computing, can the brightness value of image Img3 be suppressed to decide weight by reference noise, to produce the human eye impression preferably rear image Img5 of process.For example, be the pixel of 100 for brightness value in noise suppressed image Img3, the weight merging module 150 mixed noise suppression image Img3 and the 2nd YCbCr image Img4 is such as set as 80: 20; Because human eye is often comparatively responsive to dark portion noise, be therefore the pixel of 10 to brightness value in noise suppressed image Img3, the weight merging module 150 mixed noise suppression image Img3 and the 2nd YCbCr image Img4 is such as set as 90: 10.In one embodiment, such as can be beforehand with by those skilled in the art for the weight set by different brightness value and set and be stored as form, to make merging module 150 can quick search is corresponding in the memory cell (not illustrating at Fig. 1) weight setting value.
In the present embodiment, owing to having many same or similar treatment steps in the first rank image processing flow and second-order image processing flow, therefore same hardware (being such as colour reconstruction module 130) can be adopted but the mode of repeatedly computing, to reduce the cost in hardware designs.The present embodiment not only can allow the image after noise filtering retain image detail information, also can the discontinuous sense of image quality of removal of images, promotes natural sense and the image quality of image after process.
Second embodiment
In the present embodiment, the not natural sense of image is caused after noise filtering, also by producing many noise spots at random in the picture, and the noise spot of these tool different qualities is blended in the image after noise filtering, to reach the technology that natural sense is reappeared.
Fig. 3 is the calcspar according to the image processing apparatus shown in second embodiment of the invention.Image processing apparatus 300 is such as digital camera, slr camera, Digital Video or other have the electronic installation such as smart mobile phone, panel computer, notebook computer, desktop computer of image processing function, 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 luminance picture and chromatic diagram picture by received input picture.Noise generation module 320 is in order to produce noise pattern (noisemap).Merge module 330 luminance picture, noise pattern and chromatic diagram picture to be carried out mixing (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 realize.Wherein hardware can comprise central processing unit, chipset, microprocessor etc. and has the hardware device of image operation processing capacity or the combination of above-mentioned hardware device, and software can be then 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, the detailed step of each module declaration the present embodiment in image processing apparatus 300 of namely arranging in pairs or groups below:
Step S410, separation of images module 310 receives an input picture Img6.Wherein, this input picture Img6 is such as the 2nd YCbCr image Img4 in the first embodiment.Next, in step S420, this input picture Img6 changes by separation of images module 310, isolates luminance picture (that is, Y channel image) Img7 and chromatic diagram picture (that is, CbCr channel image) Img8.Wherein, luminance picture Img7 is sent to noise generation module 320 respectively and merges module 330; Chromatic diagram is then directly sent to as Img8 and merges module 330.
In step S430, noise generation module 320 produces corresponding noise pattern Img9 for luminance picture Img7.Wherein, the picture size (size) of noise pattern Img9 is same as the picture size of luminance picture Img7.Noise pattern Img9 is the numerical value comprising most positive signs or negative sign, and wherein each numerical value corresponds to each pixel in luminance picture Img7 respectively.
Finally, in step S440, merge module 330 and 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 first the NF that the brightness value of each pixel in luminance picture Img7 is corresponding with noise pattern Img9 to be respectively added, to produce the luminance picture comprising noise spot.Then, merge module 330 again the luminance picture comprising noise spot to be mixed as Img8 with chromatic diagram, to produce the output image Img10 with natural sense.Wherein, this input picture Img10 such as can be used as the image of input merging module 150 in the first embodiment.
Should be noted that, in luminance picture Img7, add the function that noise spot is equal to payment noise filtering, therefore these noise spots not can produce arbitrarily at random, must meet the natural viewpoint of user.That is must with reference to the hobby of human eye vision, the present embodiment is difference according to image brightness and adds the noise spot of different size, varying strength and different trend, allows user have better visual experience.Below how the noise generation module 320 describing the present embodiment with Fig. 5 and Fig. 6 in detail is produced corresponding noise pattern Img9 for luminance picture Img7.
Fig. 5 is a kind of detailed embodiment 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 (blurmask) 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 a kind of detailed embodiment producing 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, in step S610, noise generation module 320 receives a luminance picture Img7.Luminance picture Img7 is sent to single-point noise determining means 510 respectively and T_BlurMasked determining means 520 processes.
In step S620, single-point noise determining means 510 first determines respective NF scope (noiserange) and respective noise offset amount (noiseoffset) to each pixel in luminance picture, produces single-point noise figure (singlepixelnoisemap) thus.In detail, single-point noise determining means 510 first decides NF scope (-TH according to the patch characteristics of luminance picture Img7 and the brightness number of each pixel
range, TH
range), wherein TH
rangefor being greater than the positive number of 0.Then, produce a NF by tandom number generator 512 random number, wherein this NF drops on NF scope (-TH
range, TH
range) between.Next, this NF is added a corresponding noise offset amount by blender 514 again, in order to control noises mean intensity.Each pixel in luminance picture Img7, all after above-mentioned process, just can obtain single-point noise figure (singlepixelnoisemap).Wherein, noise offset amount can do setting in advance in table form by those skilled in the art, and the brightness number according to luminance picture Img7 is tabled look-up, and just can obtain noise offset amount.The function of noise offset amount also comprises enhancing picture contrast, and to make, image highlights is brighter, dark portion is darker.
On the other hand, in step S630, each pixel in T_BlurMasked determining means 520 pairs of luminance pictures determines respective T_BlurMasked (blurmask), to produce image mask indicator diagram.In detail, because the numerical value change of each noise in single-point noise figure and the apparent distribution of natural noise can be different, natural noise clustering may form the distribution of circle or other shapes.Therefore, also need to use image mask to produce the noise of different-style to single-point noise figure, so-called image mask is T_BlurMasked.
Due to noise in image style along with brightness is slightly different, and same brightness also has multiple noise style.Therefore, T_BlurMasked determining means 520 first according to the patch characteristics of luminance picture and the brightness number of each pixel, can choose corresponding T_BlurMasked set in a T_BlurMasked database.The T_BlurMasked of most tool different sizes and different pattern is stored in T_BlurMasked database.For example, T_BlurMasked can be the N*N shade of standard, N be greater than 0 positive integer.Each T_BlurMasked one numbering can be given in T_BlurMasked database, suppose in T_BlurMasked database, there are 10 groups of T_BlurMaskeds, be then numbered 1 ~ 10 respectively.
For example, 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 such as can use the T_BlurMasked being numbered 1,2 or 5 corresponding to brightness number; Be 1 such as can use the T_BlurMasked being 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 this pixel corresponding with random number from the T_BlurMasked set of correspondence, and records its numbering.After each pixel being recorded to the T_BlurMasked numbering of its correspondence, namely produce the image mask indicator diagram of the present embodiment.
Get back to Fig. 6, in step S640, the single-point noise figure of correspondence is carried out Fuzzy processing according to image mask indicator diagram by Fuzzy Processing unit 530, just can produce the noise pattern Img9 of tool different qualities.
In sum, the present invention adopts the mode in dual image process path, and the first rank image procossing is the blurred picture after producing noise filtering, and second-order image procossing is the picture rich in detail retaining image detail information and/or tool different qualities noise.By considering that two images are done weighted blend by image bright dark degree, with output packet image containing image detail information and after reaching the process of filtering noise effect.In addition, the blocky effect that produces after the image processing method that the present invention adopts can avoid general noise filtering and the discontinuous sense of removal of images, to make image tool natural character after process.
Last it is noted that above each embodiment is only in order to illustrate technical scheme of the present invention, be not intended to limit; Although with reference to foregoing embodiments to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein some or all of technical characteristic; 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;
One first rank image procossing is performed to produce one first YC format-pattern to this source images;
One second-order image procossing is performed to produce one second YC format-pattern to this source images;
A noise suppressed image is produced after one noise suppressed process is performed to this first YC format-pattern; And
After first weight process being carried out to the luminance picture in the luminance picture in this noise suppressed image and this second YC format-pattern, merge chromatic diagram picture in this second YC format-pattern to produce image after a process, 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 performing this first rank image procossing comprises:
After one Bel's noise filtering process is performed to this source images, then perform a color interpolation 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 performing this second-order image procossing comprises:
Directly this color interpolation process and this colour reconstruction process are performed to this source images, to produce this second YC format-pattern.
4. image processing method according to claim 1, is characterized in that, this source images is performed this second-order image procossing with the step producing this second YC format-pattern after, also comprise:
This second YC format-pattern is changed, to isolate a luminance picture and a colourity image;
A corresponding noise pattern is produced for this luminance picture; And
This luminance picture, noise pattern are mixed with this chromatic diagram picture, 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 each pixel in this luminance picture respectively.
7. image processing method according to claim 4, is characterized in that, the step producing this corresponding noise pattern for this luminance picture comprises:
A respective NF scope and a respective noise offset amount are determined to each pixel in this luminance picture, produces a single-point noise figure thus;
A respective T_BlurMasked is determined to each pixel in this luminance picture, 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 Fuzzy processing, to produce this noise pattern.
8. image processing method according to claim 7, is characterized in that, first determine respectively this NF scope and respectively this noise offset amount to respectively this pixel in this luminance picture respectively, the step producing this single-point noise figure thus comprises:
This NF scope is determined according to a patch characteristics of this luminance picture and a brightness number;
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 respectively this NF of each this pixel is added respectively this noise offset amount, to obtain this single-point noise figure.
9. image processing method according to claim 7, is characterized in that, determines respectively this T_BlurMasked, comprise with the step producing this image mask indicator diagram respectively this pixel in this luminance picture:
According to a patch characteristics of this luminance picture and a brightness number of each this pixel, in a T_BlurMasked database, choose a corresponding T_BlurMasked set respectively; 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, stores the T_BlurMasked of most tool different sizes and different pattern in this T_BlurMasked database.
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