CN105989583A - Image defogging method - Google Patents
Image defogging method Download PDFInfo
- Publication number
- CN105989583A CN105989583A CN201610569218.8A CN201610569218A CN105989583A CN 105989583 A CN105989583 A CN 105989583A CN 201610569218 A CN201610569218 A CN 201610569218A CN 105989583 A CN105989583 A CN 105989583A
- Authority
- CN
- China
- Prior art keywords
- image
- value
- max
- absorbance
- mist
- 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
- 238000000034 method Methods 0.000 title claims abstract description 20
- 239000003595 mist Substances 0.000 claims description 33
- 238000002835 absorbance Methods 0.000 claims description 20
- 238000003379 elimination reaction Methods 0.000 claims description 19
- 230000000717 retained effect Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 3
- 238000002834 transmittance Methods 0.000 abstract 3
- 230000008030 elimination Effects 0.000 description 10
- 238000012544 monitoring process Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 230000007850 degeneration Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses an image defogging method. The method uses a new transmittance estimation method to replace a soft matting method, the transmittance is solved through gain intervention on the three-primary color channel minimum component for a foggy image, on the premise of avoiding halation and fast effects, the approximate dark channel image intensity is solved quickly, the real-time performance of the algorithm is thus improved, a quad-tree subdivision method is used for estimating the atmospheric light value, the atmospheric light value precision is improved, and finally, through the transmittance and the atmospheric light value, defogging is carried out on multiple images. The method of the invention improves the real-time performance of defogging and the fidelity of a defogged image.
Description
Technical field
The invention belongs to Digital Image Processing and technical field of machine vision, particularly to a kind of image defogging method.
Background technology
Along with development and the progress of living standards of the people of science and technology, real life uses increasing vision
System, such as: monitoring system, intelligent transportation system etc., human life quality and demand gradually step up, these application systems and the mankind
Living closely related, direct image in human lives.
The reduction of air quality and reduce life and the industrial expansion to people of visibility cause the biggest shadow
Ring.Absorbed by turbid media due to light and scatter, out of doors the picture of shooting under vile weather so that the intensity of light reduces,
The light strong production causing optical pickocff to receive changes, and the fidelity ultimately resulting in color declines, and there is serious cross-color
With skew, and the reduction of picture contrast, image detail information loses, and definition is inadequate, and the identification of pictorial information is significantly
Reduce.Additionally, most automatic system, it is strongly dependent on the definition of input picture, and the image of degeneration can cause automatically
System cisco unity malfunction, affects and limits the work such as highway vision monitoring, intelligent navigation, remote sensing monitoring.
Therefore, to have mist image mist elimination process have broad application prospects, can be applicable to underwater photograph technical, take photo by plane, family
Outer monitoring even medical images etc., image and video through mist elimination process have more value, are conducive to many image understandings
Applying (such as aerial image) with computer vision, image is classified, and image/video is retrieved, and remote sensing and video analysis and identification, to people
Life bring a lot of facilities.In image processing field, the starting of image mist elimination is the most later.Chinese scholars carries at present
The method of the image mist elimination gone out is not the most the most perfect, it would be highly desirable to improve, and specifically how improves the real-time of mist elimination process
Property and fidelity be solve mist elimination problem key.
Summary of the invention
In order to solve the technical problem that above-mentioned background technology proposes, it is desirable to provide a kind of image defogging method, adopt
By the absorbance improved and the computational methods of air light value, improve real-time and the precision of mist elimination image of mist elimination.
In order to realize above-mentioned technical purpose, the technical scheme is that
A kind of image defogging method, comprises the following steps:
(1) calculate and have the absorbance of mist image:
T (x, y)=(1-Im(x,y))+gρ (1)
In formula (1), t (x, y) is absorbance, (x, y) represents pixel coordinate, and ρ is calibration factor, and g is gain constant:
In formula (2), | Im| and | d | is I respectivelymWith the sum of all pixels of d, corresponding ImAnd d:
D (x, y)=Im(x,y)-Id(x,y) (4)
In formula (3), IcRepresent the Color Channel of mist elimination image;
In formula (4),Ω (x, y) represent with (x, y) centered by square
Region, min represents and takes minima;
(2) use the air light value of quaternary tree close classification estimated mist image, specifically comprise the following steps that
A () will have mist image division to become some equal-sized rectangular sub blocks;
B () calculates the average pixel value of each rectangular sub blocks, retain the rectangular sub blocks that average pixel value is maximum, and by this square
The average pixel value of shape sub-block is designated as max pixel value Amax;
If (c) max pixel value AmaxLess than pixel threshold a preset, and max pixel value AmaxCorresponding rectangular sub blocks
Size more than or equal to the minimum window size preset, then returns to step (a), Further Division rectangular sub blocks, otherwise enters step
(d);
D image is transformed into YCbCr space from rgb space by (), and corresponding to the final rectangular sub blocks retained, select this square
The maximum of shape sub-block luminance component is as air light value Ac;
(3) the air light value that the absorbance obtained according to step (1) and step (2) obtain, carries out mist elimination process to image:
In formula (5), J represents the image after mist elimination process, and I is for having mist image, t0For absorbance threshold value, max represents and takes
Big value.
Further, in step (1), the span of calibration factor ρ is [0.8,1].
Further, the value of calibration factor ρ is 0.9.
Further, in step (3), absorbance threshold value t0Span be [0.05,0.15].
Further, absorbance threshold value t0Value be 0.1.
The beneficial effect that employing technique scheme is brought:
The present invention, compared in original dark algorithm, uses new absorbance estimation algorithm to replace soft stingy figure method, passes through
There is the gain intervention of primary display channels minimum component of mist image to try to achieve absorbance, can be before avoiding halation and fast effect
Put, quickly try to achieve approximation dark channel image intensity, thus improve the real-time of algorithm, and use the estimation of quaternary tree close classification big
Gas light value so that the precision of air light value improves, thus reaches the effect of the image mist elimination that real-time is good and fidelity is high.
Accompanying drawing explanation
Fig. 1 is the basic flow sheet of the present invention.
Fig. 2 is the flow chart calculating atmosphere light value in the present invention.
Detailed description of the invention
Below with reference to accompanying drawing, technical scheme is described in detail.
As it is shown in figure 1, a kind of image defogging method, comprise the following steps:
Step 1, calculate and have the absorbance of mist image:
T (x, y)=(1-Im(x,y))+gρ (1)
In formula (1), (x, y) is absorbance to t, and (x, y) represents pixel coordinate, and ρ is calibration factor, and the value of ρ generally exists
In [0.8,1], the present embodiment can make ρ=0.9, and g is gain constant:
In formula (2), | Im| and | d | is I respectivelymWith the sum of all pixels of d, corresponding ImAnd d:
D (x, y)=Im(x,y)-Id(x,y) (4)
In formula (3), IcRepresent the Color Channel of mist elimination image;
In formula (4),Ω (x, y) represent with (x, y) centered by square
Region, min represents and takes minima.
Step 2, use quaternary tree close classification estimated mist image air light value, concrete steps as shown in Figure 2:
A () will have mist image division to become some equal-sized rectangular sub blocks;
B () calculates the average pixel value of each rectangular sub blocks, retain the rectangular sub blocks that average pixel value is maximum, and by this square
The average pixel value of shape sub-block is designated as max pixel value Amax;
If (c) max pixel value AmaxLess than pixel threshold a preset, and max pixel value AmaxCorresponding rectangular sub blocks
Size more than or equal to the minimum window size preset, then returns to step (a), Further Division rectangular sub blocks, otherwise enters step
(d);
D image is transformed into YCbCr space from rgb space by (), and corresponding to the final rectangular sub blocks retained, select this square
The maximum of shape sub-block luminance component is as air light value Ac。
The air light value that step 3, the absorbance obtained according to step 1 and step 2 obtain, carries out mist elimination process to image:
In formula (5), J represent mist elimination process after image, I is for there being mist image, and max represents and takes maximum, t0For absorbance
Threshold value, t0Span be [0.05,0.15], the present embodiment can make t0=0.1, be used for limiting absorbance t (x, y), due to
As t, (x, when y) → 0, (x, y) (x, y) also can level off to 0 to t to J so that mist elimination image comprises noise, for avoiding sending out of this situation
Raw, t is set0Improve the quality of mist elimination image.
Above example is only the technological thought that the present invention is described, it is impossible to limit protection scope of the present invention with this, every
The technological thought proposed according to the present invention, any change done on the basis of technical scheme, each fall within scope
Within.
Claims (5)
1. an image defogging method, it is characterised in that comprise the following steps:
(1) calculate and have the absorbance of mist image:
T (x, y)=(1-Im(x,y))+gρ (1)
In formula (1), t (x, y) is absorbance, (x, y) represents pixel coordinate, and ρ is calibration factor, and g is gain constant:
In formula (2), | Im| and | d | is I respectivelymWith the sum of all pixels of d, corresponding ImAnd d:
D (x, y)=Im(x,y)-Id(x,y) (4)
In formula (3), IcRepresent the Color Channel of input picture;
In formula (4),Ω (x, y) represent with (x, y) centered by square region,
Min represents and takes minima;
(2) use the air light value of quaternary tree close classification estimated mist image, specifically comprise the following steps that
A () will have mist image division to become some equal-sized rectangular sub blocks;
B () calculates the average pixel value of each rectangular sub blocks, retain the rectangular sub blocks that average pixel value is maximum, and by this rectangle
The average pixel value of block is designated as max pixel value Amax;
If (c) max pixel value AmaxLess than pixel threshold a preset, and max pixel value AmaxThe size of corresponding rectangular sub blocks
More than or equal to the minimum window size preset, then return to step (a), Further Division rectangular sub blocks, otherwise enter step (d);
D image is transformed into YCbCr space from rgb space by (), and corresponding to the final rectangular sub blocks retained, select this rectangle
The maximum of Block Brightness component is as air light value Ac;
(3) the air light value that the absorbance obtained according to step (1) and step (2) obtain, carries out mist elimination process to image:
In formula (5), J represents the image after mist elimination process, and I is for having mist image, t0For absorbance threshold value, max represents and takes maximum.
A kind of image defogging method, it is characterised in that: in step (1), calibration factor ρ takes
Value scope is [0.8,1].
A kind of image defogging method, it is characterised in that: the value of calibration factor ρ is 0.9.
A kind of image defogging method, it is characterised in that: in step (3), absorbance threshold value t0's
Span is [0.05,0.15].
A kind of image defogging method, it is characterised in that: absorbance threshold value t0Value be 0.1.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610569218.8A CN105989583B (en) | 2016-07-19 | 2016-07-19 | A kind of image defogging method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610569218.8A CN105989583B (en) | 2016-07-19 | 2016-07-19 | A kind of image defogging method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN105989583A true CN105989583A (en) | 2016-10-05 |
CN105989583B CN105989583B (en) | 2018-07-24 |
Family
ID=57044532
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201610569218.8A Expired - Fee Related CN105989583B (en) | 2016-07-19 | 2016-07-19 | A kind of image defogging method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN105989583B (en) |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106846260A (en) * | 2016-12-21 | 2017-06-13 | 常熟理工学院 | Video defogging method in a kind of computer |
CN107316284A (en) * | 2017-07-19 | 2017-11-03 | 山东财经大学 | Intense light source hypograph defogging method and device |
CN108765337A (en) * | 2018-05-28 | 2018-11-06 | 青岛大学 | A kind of single width color image defogging processing method based on dark primary priori Yu non local MTV models |
CN112083716A (en) * | 2019-06-13 | 2020-12-15 | 中国电信股份有限公司 | Navigation method, device and system based on machine vision |
CN112949389A (en) * | 2021-01-28 | 2021-06-11 | 西北工业大学 | Haze image target detection method based on improved target detection network |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104252698A (en) * | 2014-06-25 | 2014-12-31 | 西南科技大学 | Semi-inverse method-based rapid single image dehazing algorithm |
-
2016
- 2016-07-19 CN CN201610569218.8A patent/CN105989583B/en not_active Expired - Fee Related
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104252698A (en) * | 2014-06-25 | 2014-12-31 | 西南科技大学 | Semi-inverse method-based rapid single image dehazing algorithm |
Non-Patent Citations (4)
Title |
---|
KAIMING HE ET AL: "Single Image Haze Removal Using Dark Channel Prior", 《IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE》 * |
SHIH-CHIA HUANG ET AL: "Visibility Restoration of Single Hazy Images Captured in Real-World Weather Conditions", 《IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY》 * |
SUNGMIN LEE ET AL: "A review on dark channel prior based image dehazing algorithms", 《EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING》 * |
YUAN-KAI WANG ET AL: "Single Image Defogging by Multiscale Depth Fusion", 《IEEE TRANSACTIONS ON IMAGE PROCESSING》 * |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106846260A (en) * | 2016-12-21 | 2017-06-13 | 常熟理工学院 | Video defogging method in a kind of computer |
CN106846260B (en) * | 2016-12-21 | 2019-06-07 | 常熟理工学院 | Video defogging method in a kind of computer |
CN107316284A (en) * | 2017-07-19 | 2017-11-03 | 山东财经大学 | Intense light source hypograph defogging method and device |
CN108765337A (en) * | 2018-05-28 | 2018-11-06 | 青岛大学 | A kind of single width color image defogging processing method based on dark primary priori Yu non local MTV models |
CN108765337B (en) * | 2018-05-28 | 2021-06-15 | 青岛大学 | Single color image defogging processing method based on dark channel prior and non-local MTV model |
CN112083716A (en) * | 2019-06-13 | 2020-12-15 | 中国电信股份有限公司 | Navigation method, device and system based on machine vision |
CN112949389A (en) * | 2021-01-28 | 2021-06-11 | 西北工业大学 | Haze image target detection method based on improved target detection network |
Also Published As
Publication number | Publication date |
---|---|
CN105989583B (en) | 2018-07-24 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN102750674B (en) | Video image defogging method based on self-adapting allowance | |
CN105989583A (en) | Image defogging method | |
CN102063706B (en) | Rapid defogging method | |
CN107301623B (en) | Traffic image defogging method and system based on dark channel and image segmentation | |
CN107767354A (en) | A kind of image defogging algorithm based on dark primary priori | |
CN102722868B (en) | Tone mapping method for high dynamic range image | |
CN104240194B (en) | A kind of enhancement algorithm for low-illumination image based on parabolic function | |
CN105023256B (en) | A kind of image defogging method and system | |
CN107451966B (en) | Real-time video defogging method implemented by guiding filtering through gray level image | |
CN106897981A (en) | A kind of enhancement method of low-illumination image based on guiding filtering | |
CN104809709A (en) | Single-image self-adaptation defogging method based on domain transformation and weighted quadtree decomposition | |
CN103077504B (en) | A kind of image defogging method capable based on self-adaptation illumination calculation | |
CN103218778A (en) | Image and video processing method and device | |
CN103440674B (en) | A kind of rapid generation of digital picture wax crayon specially good effect | |
CN102831591A (en) | Gaussian filter-based real-time defogging method for single image | |
CN108154492B (en) | A kind of image based on non-local mean filtering goes haze method | |
CN104299192A (en) | Single image defogging method based on atmosphere light scattering physical model | |
CN104021527B (en) | Rain and snow removal method in image | |
CN104867121A (en) | Fast image defogging method based on dark channel prior and Retinex theory | |
CN103914820A (en) | Image haze removal method and system based on image layer enhancement | |
He et al. | Single image dehazing with white balance correction and image decomposition | |
CN104331867B (en) | The method, device and mobile terminal of image defogging | |
CN106023108A (en) | Image defogging algorithm based on boundary constraint and context regularization | |
CN110136079A (en) | Image defogging method based on scene depth segmentation | |
CN103106671B (en) | Method for detecting interested region of image based on visual attention mechanism |
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 | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180724 Termination date: 20210719 |
|
CF01 | Termination of patent right due to non-payment of annual fee |