CN105957030B - One kind being applied to the enhancing of thermal infrared imager image detail and noise suppressing method - Google Patents
One kind being applied to the enhancing of thermal infrared imager image detail and noise suppressing method Download PDFInfo
- Publication number
- CN105957030B CN105957030B CN201610265370.7A CN201610265370A CN105957030B CN 105957030 B CN105957030 B CN 105957030B CN 201610265370 A CN201610265370 A CN 201610265370A CN 105957030 B CN105957030 B CN 105957030B
- Authority
- CN
- China
- Prior art keywords
- image
- enhancing
- detail
- edge
- details
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000002708 enhancing effect Effects 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000000605 extraction Methods 0.000 claims abstract description 20
- 238000009499 grossing Methods 0.000 claims description 12
- 230000009466 transformation Effects 0.000 claims description 6
- 230000004927 fusion Effects 0.000 claims 1
- 230000002401 inhibitory effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 230000003014 reinforcing effect Effects 0.000 description 1
Classifications
-
- G06T5/70—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
-
- G06T5/73—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10048—Infrared image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20221—Image fusion; Image merging
Abstract
It is applied to the enhancing of thermal infrared imager image detail and noise suppressing method the invention discloses a kind of, comprising the following steps: the edge image extracted, detail pictures and primary image are carried out details enhancing by the extraction of S1 edge image, the extraction of S2 detail pictures, the extraction of S3 primary image, S4 respectively；S5 merges enhanced edge image, detail pictures and primary image, obtains final enhancing image；The present invention provides one kind to be applied to the enhancing of thermal infrared imager image detail and noise suppressing method, extracting and handling respectively to edge image, detail pictures and basic tomographic image, again will treated that image merges so that treated picture contrast and details significantly increase；Apparent edge is eliminated in the corresponding levels of detail of the second smoothed image simultaneously, is completely not in edge gray inversion phenomenon.
Description
Technical field
The present invention relates to one kind to be applied to the enhancing of thermal infrared imager image detail and noise suppressing method.
Background technique
Modern high performance thermal infrared imager can obtain the very big raw image data of dynamic range, the general 12-16 of bit wide
Position, but show that the dynamic range bit wide of equipment is generally 8, so must be to compression raw image data so general display
Big profile can be only shown in equipment；Basic background information；The a large amount of minutia of scene is had lost, so as to cause important details
Information is lost.
The image de-noising method of the prior art has: color histogram equalization utilizes the probability density distribution of entire image
Function calculates integral density distribution function, crosses grey-scale map and realizes that processing picture superposition this method can be to histogram
The high moral gray level of probability density realizes apparent reinforcing effect, and is difficult effectively to enhance to low probability density gray level, or even ash
Degree grade merging leads to loss in detail.
Automatic growth control, the first extreme value in rejecting scene, then overall dynamic range is mapped to the gray scale sky of 0-255
Between, this method causes contrast low, and loss in detail is serious；
Image layered processing, mainly image layered levels of detail and Primary layer, this method operand is big, is easy to appear side
Edge gray inversion enhances minor detail unobvious.
Summary of the invention
It is applied to thermal infrared imager image detail it is an object of the invention to overcome the deficiencies of the prior art and provide one kind to increase
Strong and noise suppressing method, extracting and handling respectively to edge image, detail pictures and basic tomographic image, then will processing
Image afterwards is merged, so that treated picture contrast and details significantly increase.
The purpose of the present invention is achieved through the following technical solutions: one kind being applied to thermal infrared imager image detail and increases
Strong and noise suppressing method, comprising the following steps: including edge image extraction step S1, detail pictures extraction step S2, basic
Image extracting step S3, details enhance step S4 and image co-registration step S5；
The edge image extraction step S1 includes following sub-step:
S11. original image I is handled using first edge smoothing filter, obtains the first smoothed image T1；
S12. edge image DI1 is extracted:
DI1=I-T1；
It is primarily present in the first smoothed image T1 further, for most noise, is subtracted using original graph image I
It goes the first smoothed image T1 to extract edge image, good inhibiting effect can be played to noise.
The detail pictures extraction step S2 includes following sub-step:
S21. original image I is handled using second edge smoothing filter, obtains the second smoothed image T2；
S22. the corresponding levels of detail DI2 of the second smoothed image T2 is calculated:
DI2=I-T2；
Further, since most noise is primarily present in the second smoothed image T2, second is subtracted with original image I
Smoothed image T2 obtains levels of detail, can play good inhibiting effect to noise.
S23. detail pictures DI is extracted:
DI=DI2-DI1；
The primary image extraction step S3 includes: to be handled using holding edge filter device original image I, is obtained
To basic tomographic image T3；
The details enhances step S4
S41. details enhancing processing, the edge image DI1 ' enhanced are carried out to edge image DI1；
S42. details enhancing processing, the detail pictures DI ' enhanced are carried out to detail pictures DI；
S43. details enhancing processing, the primary image T3 ' enhanced are carried out to primary image P3；
The image co-registration step S5 includes: by edge image DI1 ', detail pictures DI ' and the primary image of enhancing
T3 ' is merged, and final enhancing image DOUT_B is obtained:
DOUT_B=T3 '+P1*DI0 '+P2*DI '；
In formula, P1, P2 are proportionality coefficients, and the value interval of P1 is (0,3), and the value interval of P2 is (0,2).
Details enhancing, the edge graph enhanced are carried out to edge image DI1 by the transformation of GAMMA curve in step S41
As DI1 '.
Details enhancing, the detail pictures enhanced are carried out to detail pictures DI by the transformation of GAMMA curve in step S42
DI′。
The step S43 includes: by basic tomographic image T3 to degree of comparing enhancing, and pixel in tomographic image T3 substantially
Point intensity value ranges are compressed to the gray space of 0-255.
Described one kind is applied to the enhancing of thermal infrared imager image detail and noise suppressing method, further includes one final
Enhance image number field restriction step S6, including following sub-step:
S61. pixel of the gray value less than 0 in final enhancing image DOUT_B is searched, and its gray value is limited to
0；
S62. it searches gray value in final enhancing image DOUT_B and is greater than 255 pixel, and its gray value is limited
It is 255.
The beneficial effects of the present invention are: passing through extracting respectively to edge image, detail pictures and basic tomographic image
And processing, then will treated that image merges so that treated picture contrast and details significantly increase；Simultaneously the
Apparent edge is eliminated in the corresponding levels of detail of two smoothed images, is completely not in edge gray inversion phenomenon.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing, but protection scope of the present invention is not limited to
It is as described below.
As shown in Figure 1, a kind of be applied to the enhancing of thermal infrared imager image detail and noise suppressing method, including following step
It is rapid: to enhance step including edge image extraction step S1, detail pictures extraction step S2, primary image extraction step S3, details
S4 and image co-registration step S5；
The edge image extraction step S1 includes following sub-step:
S11. original image I is handled using first edge smoothing filter, obtains the first smoothed image T1；
S12. edge image DI1 is extracted:
DI1=I-T1；
The detail pictures extraction step S2 includes following sub-step:
S21. original image I is handled using second edge smoothing filter, obtains the second smoothed image T2；
S22. the corresponding levels of detail DI2 of the second smoothed image T2 is calculated:
DI2=I-T2；
S23. detail pictures DI is extracted:
DI=DI2-DI1；
This step has subtracted edge image DI1 and (has eliminated apparent in the corresponding levels of detail DI2 of the second smoothed image T2
Marginal information), it completely will not edge gray inversion phenomenon.
The primary image extraction step S3 includes: to be handled using holding edge filter device original image I, is obtained
To basic tomographic image T3；
The details enhances step S4
S41. details enhancing processing, the edge image DI1 ' enhanced are carried out to edge image DI1；
S42. details enhancing processing, the detail pictures DI ' enhanced are carried out to detail pictures DI；
S43. details enhancing processing, the primary image T3 ' enhanced are carried out to primary image P3；
The image co-registration step S5 includes: by edge image DI1 ', detail pictures DI ' and the primary image of enhancing
T3 ' is merged, and final enhancing image DOUT_B is obtained:
DOUT_B=T3 '+P1*DI0 '+P2*DI '；
In formula, P1, P2 are proportionality coefficients, and the value interval of P1 is (0,3), and the value interval of P2 is (0,2).
Details enhancing, the edge graph enhanced are carried out to edge image DI1 by the transformation of GAMMA curve in step S41
As DI1 '.
Details enhancing, the detail pictures enhanced are carried out to detail pictures DI by the transformation of GAMMA curve in step S42
DI′。
The step S43 includes: by basic tomographic image T3 to degree of comparing enhancing, and pixel in tomographic image T3 substantially
Point intensity value ranges are compressed to the gray space of 0-255.
Described one kind is applied to the enhancing of thermal infrared imager image detail and noise suppressing method, further includes one final
Enhance image number field restriction step S6, including following sub-step:
S61. pixel of the gray value less than 0 in final enhancing image DOUT_B is searched, and its gray value is limited to
0；
S62. it searches gray value in final enhancing image DOUT_B and is greater than 255 pixel, and its gray value is limited
It is 255.
Embodiment 1 successively detects entire image by the sliding window of (2k+1) * (2k+1):
In formula, x indicates that image line coordinate, y indicate image column coordinate, k window size；
- k:k indicates integer of the value of s and t between-k to k, i.e. s=-k ,-k+1 ..., k-1, k；
T=-k ,-k+1 ..., k-1, k.
It indicates, is s=-k ,-k+1 ..., k-1 in s value range, k, t value range is
T=-k ,-k+1 ..., k-1, when k, the maximum value of I (x+s, y+t), i.e. pixel maximum in current window.
Similarly,The minimum value for indicating I (x+s, y+t), i.e., the pixel in current window are minimum
Value.
Delta (x, y) in formula (1) indicates that image seeks local delta value to sliding (2k+1) * (2k+1) window, i.e.,
The difference of maximum value and most lower value, can characterize details and edge in image, under normal circumstances:
The region of delta < 100 is not include details smooth region；The region of 100 < delta < 2000 is mainly details；
The region of Delta > 2000 is obvious edge.
Parameter eps is introduced, delta (x, y) is normalized, is obtained:
It can be seen that according to formula (2)
If delta (x, y) >=eps, a (x, y) >=0.5；
Delta if (x, y) < eps, a (x, y) < 0.5；
Image is asked to seek local mean value to sliding (2k+1) * (2k+1) window:
For edge smoothing filter:
I_s (x, y)=I (x, y) * (1-a (x, y))+I_avg (x, y) * a (x, y) (4)
I_s (x, y) indicates that edge smoothing filter exports image in formula, and I (x, y) indicates original image.
It can be seen that the more big then average ratio of a (x, y) is with regard to big, image original value ratio is smaller, that is to say, that the area of delta < eps
The smaller closer original image of domain delta；The region Delta=eps original image and mean value respectively account for half；The region delta of delta > eps is got over
It is big more smooth；Therefore first edge smoothing filter and second edge smoothing filter can by different eps is set come
It realizes, such as the settable eps of first edge smoothing filter is between 2000~5000, second edge smoothing filter is settable
Eps is between 30~100.
For holding edge filter device:
I_bs (x, y)=I_avg (x, y) * (1-a (x, y))+I (x, y) * a (x, y), (5)
I_bs (x, y) is the image of holding edge filter device output.
From formula (5): smooth delta compares zonule, the region for keeping delta bigger can reach edge
Purpose, such as eps is kept to may be configured as 30~100.
Claims (5)
1. one kind is applied to the enhancing of thermal infrared imager image detail and noise suppressing method, it is characterised in that: the following steps are included:
Including edge image extraction step S1, detail pictures extraction step S2, primary image extraction step S3, details enhancing step S4 and
Image co-registration step S5；
The edge image extraction step S1 includes following sub-step:
S11. original image I is handled using first edge smoothing filter, obtains the first smoothed image T1；
S12. edge image DI1 is extracted:
DI1=I-T1；
The detail pictures extraction step S2 includes following sub-step:
S21. original image I is handled using second edge smoothing filter, obtains the second smoothed image T2；
S22. the corresponding levels of detail DI2 of the second smoothed image T2 is calculated:
DI2=I-T2；
S23. detail pictures DI is extracted:
DI=DI2-DI1；
The primary image extraction step S3 includes: to be handled using holding edge filter device original image I, obtains base
This tomographic image T3；
The details enhances step S4
S41. details enhancing processing, the edge image DI1 ' enhanced are carried out to edge image DI1；
S42. details enhancing processing, the detail pictures DI ' enhanced are carried out to detail pictures DI；
S43. details enhancing processing, the primary image T3 ' enhanced are carried out to primary image T3；
The image co-registration step S5 include: by edge image DI1 ', the detail pictures DI ' of enhancing and primary image T3 ' into
Row fusion, obtains final enhancing image DOUT_B:
DOUT_B=T3 '+P1*DI1 '+P2*DI '；
In formula, P1, P2 are proportionality coefficients, and the value interval of P1 is (0,3), and the value interval of P2 is (0,2).
2. it is according to claim 1 a kind of applied to the enhancing of thermal infrared imager image detail and noise suppressing method, it is special
Sign is: carrying out details enhancing, the edge image enhanced to edge image DI1 by the transformation of GAMMA curve in step S41
DI1′。
3. it is according to claim 1 a kind of applied to the enhancing of thermal infrared imager image detail and noise suppressing method, it is special
Sign is: carrying out details enhancing, the detail pictures enhanced to detail pictures DI by the transformation of GAMMA curve in step S42
DI′。
4. it is according to claim 1 a kind of applied to the enhancing of thermal infrared imager image detail and noise suppressing method, it is special
Sign is: the step S43 includes: to enhance basic tomographic image T3 degree of comparing, by pixel in basic tomographic image T3
Intensity value ranges are compressed to 0-255.
5. it is according to claim 1 a kind of applied to the enhancing of thermal infrared imager image detail and noise suppressing method, it is special
Sign is:
Further include a final enhancing image number field restriction step S6, including following sub-step:
S61. pixel of the gray value less than 0 in final enhancing image DOUT_B is searched, and its gray value is limited to 0；
S62. it searches gray value in final enhancing image DOUT_B and is greater than 255 pixel, and its gray value is limited to
255。
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610265370.7A CN105957030B (en) | 2016-04-26 | 2016-04-26 | One kind being applied to the enhancing of thermal infrared imager image detail and noise suppressing method |
| PCT/CN2016/083220 WO2017185445A1 (en) | 2016-04-26 | 2016-05-25 | Image detail enhancement and noise suppression method applied to infrared thermal imager |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610265370.7A CN105957030B (en) | 2016-04-26 | 2016-04-26 | One kind being applied to the enhancing of thermal infrared imager image detail and noise suppressing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105957030A CN105957030A (en) | 2016-09-21 |
| CN105957030B true CN105957030B (en) | 2019-03-22 |
Family
ID=56916503
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610265370.7A Active CN105957030B (en) | 2016-04-26 | 2016-04-26 | One kind being applied to the enhancing of thermal infrared imager image detail and noise suppressing method |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN105957030B (en) |
| WO (1) | WO2017185445A1 (en) |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106504294B (en) * | 2016-10-17 | 2019-04-26 | 浙江工业大学 | RGBD image vector method based on diffusion profile |
| CN108062745B (en) * | 2016-11-08 | 2022-01-11 | 北京机电工程研究所 | Method for enhancing spatial resolution capability of large forward-leaning SAR (synthetic aperture radar) image of aircraft platform |
| CN107464229A (en) * | 2017-08-15 | 2017-12-12 | 天津津航技术物理研究所 | A kind of infrared image numerical details Enhancement Method for suppressing noise |
| CN107784637B (en) * | 2017-09-30 | 2020-05-12 | 烟台艾睿光电科技有限公司 | Infrared image enhancement method |
| CN109544490B (en) * | 2018-10-17 | 2021-07-13 | 北京达佳互联信息技术有限公司 | Image enhancement method, device and computer readable storage medium |
| CN109559286B (en) * | 2018-11-19 | 2022-12-06 | 电子科技大学 | Variance gradient constraint method infrared image edge preserving denoising method |
| CN109741276B (en) * | 2018-12-28 | 2022-09-13 | 华中科技大学鄂州工业技术研究院 | Infrared image base layer processing method and system based on filtering layered framework |
| CN109919865B (en) * | 2019-02-20 | 2022-10-11 | 中国科学院上海微系统与信息技术研究所 | Image multi-layer detail enhancement method |
| US11346938B2 (en) | 2019-03-15 | 2022-05-31 | Msa Technology, Llc | Safety device for providing output to an individual associated with a hazardous environment |
| JP7252847B2 (en) * | 2019-07-08 | 2023-04-05 | 株式会社日立製作所 | Motion tracking device, radiotherapy system, and method of operating motion tracking device |
| CN111833260A (en) * | 2020-05-29 | 2020-10-27 | 红鼎互联(广州)信息科技有限公司 | Image detail enhancement and noise suppression method applied to thermal infrared imager |
| CN111968068A (en) * | 2020-08-18 | 2020-11-20 | 杭州海康微影传感科技有限公司 | Thermal imaging image processing method and device |
| CN113379640B (en) * | 2021-06-25 | 2023-06-27 | 哈尔滨工业大学 | Multi-stage filtering image denoising method integrating edge information |
| CN113763264B (en) * | 2021-08-09 | 2024-03-15 | 云南北方光电仪器有限公司 | Image processing method and storage medium based on positive and negative polarity detail layer separation |
| WO2023138273A1 (en) * | 2022-01-18 | 2023-07-27 | 武汉联影医疗科技有限公司 | Image enhancement method and system |
| CN115937016B (en) * | 2022-10-31 | 2023-07-21 | 哈尔滨理工大学 | Contrast enhancement method for guaranteeing image details |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101197910A (en) * | 2006-12-05 | 2008-06-11 | 广达电脑股份有限公司 | Image sharpening device and method |
| CN102789636A (en) * | 2012-08-01 | 2012-11-21 | 中山大学 | Method for enhancing image details on basis of multiscale combined bilateral grid smooth model |
| CN103440630A (en) * | 2013-09-02 | 2013-12-11 | 南京理工大学 | Large-dynamic-range infrared image display and detail enhancement method based on guiding filter |
| CN104021532A (en) * | 2014-06-19 | 2014-09-03 | 电子科技大学 | Image detail enhancement method of infrared image |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101727658B (en) * | 2008-10-14 | 2012-12-26 | 深圳迈瑞生物医疗电子股份有限公司 | Image processing method and device |
| CN103514583B (en) * | 2012-06-30 | 2016-08-24 | 华为技术有限公司 | Image sharpening method and equipment |
| US9053531B2 (en) * | 2013-02-28 | 2015-06-09 | Sharp Laboratories Of America, Inc. | Multi layered image enhancement technique |
| CN103971340A (en) * | 2014-05-15 | 2014-08-06 | 中国科学院光电技术研究所 | High bit-wide digital image dynamic range compression and detail enhancement method |
| CN105279746B (en) * | 2014-05-30 | 2018-01-26 | 西安电子科技大学 | A kind of more exposure image fusion methods based on bilateral filtering |
| CN104537632B (en) * | 2014-12-26 | 2017-07-11 | 武汉大学 | Infrared image histogram enhancement method based on edge extracting |
| CN104700372B (en) * | 2015-03-19 | 2017-08-04 | 天津大学 | Based on edge-protected weighting anisotropic diffusion filtering method |
-
2016
- 2016-04-26 CN CN201610265370.7A patent/CN105957030B/en active Active
- 2016-05-25 WO PCT/CN2016/083220 patent/WO2017185445A1/en active Application Filing
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101197910A (en) * | 2006-12-05 | 2008-06-11 | 广达电脑股份有限公司 | Image sharpening device and method |
| CN102789636A (en) * | 2012-08-01 | 2012-11-21 | 中山大学 | Method for enhancing image details on basis of multiscale combined bilateral grid smooth model |
| CN103440630A (en) * | 2013-09-02 | 2013-12-11 | 南京理工大学 | Large-dynamic-range infrared image display and detail enhancement method based on guiding filter |
| CN104021532A (en) * | 2014-06-19 | 2014-09-03 | 电子科技大学 | Image detail enhancement method of infrared image |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2017185445A1 (en) | 2017-11-02 |
| CN105957030A (en) | 2016-09-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105957030B (en) | One kind being applied to the enhancing of thermal infrared imager image detail and noise suppressing method | |
| CN103440630B (en) | Show and detail enhancing method based on the Larger Dynamic scope infrared image guiding wave filter | |
| Lee et al. | Contrast enhancement based on layered difference representation of 2D histograms | |
| EP3087730B1 (en) | Method for inverse tone mapping of an image | |
| CN104700376B (en) | Image histogram equalization Enhancement Method based on gamma correction and smothing filtering | |
| US9406148B2 (en) | Image processing method and apparatus, and shooting terminal | |
| WO2016206087A1 (en) | Low-illumination image processing method and device | |
| CN104268843B (en) | Image self-adapting enhancement method based on histogram modification | |
| Fan et al. | Homomorphic filtering based illumination normalization method for face recognition | |
| CN106169181B (en) | A kind of image processing method and system | |
| Shin et al. | Efficient naturalness restoration for non‐uniform illumination images | |
| US8406559B2 (en) | Method and system for enhancing image sharpness based on local features of image | |
| US8737762B2 (en) | Method for enhancing image edge | |
| CN108133463B (en) | Noise reduction method and system for histogram equalization image | |
| CN104867122A (en) | Infrared self-adaptive non-uniformity correction and detail enhanced cascade processing method | |
| Wang et al. | Enhancement for dust-sand storm images | |
| Gupta et al. | Histogram based image enhancement techniques: a survey | |
| CN112991197B (en) | Low-illumination video enhancement method and device based on detail preservation of dark channel | |
| Babakhani et al. | Automatic gamma correction based on average of brightness | |
| CN108961172A (en) | A kind of method for enhancing picture contrast based on Gamma correction | |
| CN105631854A (en) | FPGA platform-based self-adaptive image definition evaluation algorithm | |
| CN115660976A (en) | Image processing method, device, equipment and computer readable storage medium | |
| Mu et al. | Low and non-uniform illumination color image enhancement using weighted guided image filtering | |
| WO2017128646A1 (en) | Image processing method and device | |
| CN113744294A (en) | Image processing method and related device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |