CN112837250B - Infrared image self-adaptive enhancement method based on generalized histogram equalization - Google Patents
Infrared image self-adaptive enhancement method based on generalized histogram equalization Download PDFInfo
- Publication number
- CN112837250B CN112837250B CN202110108751.5A CN202110108751A CN112837250B CN 112837250 B CN112837250 B CN 112837250B CN 202110108751 A CN202110108751 A CN 202110108751A CN 112837250 B CN112837250 B CN 112837250B
- Authority
- CN
- China
- Prior art keywords
- histogram
- image
- generalized
- complexity
- gray level
- 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
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000013507 mapping Methods 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000005498 polishing Methods 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000003044 adaptive effect Effects 0.000 claims description 5
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000013459 approach Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000003331 infrared imaging Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000009529 body temperature measurement Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration using histogram techniques
-
- 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
- G06T2207/20004—Adaptive image processing
-
- 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
- G06T2207/20048—Transform domain processing
-
- 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
- G06T2207/20172—Image enhancement details
- G06T2207/20208—High dynamic range [HDR] image processing
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
An infrared image self-adaptive enhancement method based on generalized histogram equalization comprises the following steps: carrying out global point polishing on the statistical histogram of the input high dynamic range infrared image; calculating the local complexity of each pixel and the overall complexity of the image; and constructing a generalized histogram according to the calculated local complexity and the overall complexity, and carrying out equalization processing on the generalized histogram. Compared with the traditional generalized histogram local complexity-fractional term mapping curve based on the e index, the generalized histogram local complexity-fractional term mapping curve based on the e index is simplified, and the engineering practice is facilitated. The parameters capable of representing the overall complexity of the image are used for calculating the upper limit threshold and the lower limit threshold of the mapping curve of the local complexity-fractional term of the generalized histogram, so that the self-adaption can be realized for the images of different scenes. The invention performs remainder term average distribution on the effective gray level obtained after global dotting, which can effectively improve the whole contrast ratio of partial scenes with few gray levels containing less pixels.
Description
Technical Field
The invention relates to the field of infrared image processing, in particular to an infrared image self-adaptive enhancement method based on generalized histogram equalization.
Background
Compared with a visible light imaging system, the infrared imaging system can work all day long, particularly at night, and can reflect the temperature information of the infrared imaging system through the gray value of different areas of the image, so that the infrared imaging system is widely applied to various military and civil fields such as military target detection, security monitoring, human body temperature measurement monitoring and the like, and has a trend of extending from the military and civil fields along with the increase of the demand of the civil field. However, the contrast and signal-to-noise ratio of the original high dynamic range infrared image (typically greater than 12 bits) is generally low and cannot be displayed directly on a normal 8-bit display device. Therefore, it is necessary to perform dynamic range compression and enhancement processing on the original infrared image to meet the subsequent requirements of target identification, detection and tracking.
The traditional infrared image enhancement mode is mostly based on global mapping, that is, mapping is performed one by one according to the size of the gray level of an original image to keep image thermal distribution information, dynamic range compression is performed on the image, and meanwhile, contrast is improved, histogram Equalization (HE) and an improved algorithm thereof are one of the most representative methods, and the essence is that the gray level of most pixels in the image is stretched, and the gray level of few pixels is combined, so that image background noise is amplified easily, and detail information is excessively combined and lost. The prior art mostly modifies the histogram, for example, modifies the histogram by using a plateau threshold, which can improve the problem to different degrees, but because the essence of the conventional histogram is not changed, i.e. the stretching and compressing operations of the gray levels are controlled according to the number of pixels included in the same gray level, the problem cannot be completely solved, and the scene adaptation is not enough. In a conventional histogram, the pixels are considered to contribute equally to the histogram statistics, i.e. each gray level appears once with a corresponding ordinate plus 1. However, in the background, although it contains more pixels, in practice the local complexity of these pixels tends to be low; and for the places with rich details, the local complexity of the pixels is higher although the pixels are few. In theory, it is more reasonable to consider the local complexity of each pixel in constructing the histogram, and the higher the complexity, the more the contribution to the histogram approaches 1, and vice versa approaches 0. Based on the above thought, b.w.yoon et al proposed the concept of a generalized histogram to overcome the shortcomings of the conventional method from the viewpoint of modifying the definition of the histogram. In the generalized histogram, each count value is divided into two terms: and (3) constructing a fractional term and a remainder term according to the local complexity of each pixel, and then uniformly distributing the remainder term (1-fractional term) to each gray level to obtain a final generalized histogram.
The core of the generalized histogram is the construction of fractional terms, and usually the mapping of local complexity c (i, j) to fractional term r (i, j) is performed according to an e-exponential curve, i.e. r (i, j) =1-e -γc(i,j) Wherein γ is a constant. In the prior art, the algorithm is improved from the perspective of how to construct a local complexity factor, only the gamma value is selected according to experience and is regarded as a constant, and the image is easy to be amplified in background noise or the details are not effectively enhanced at two extremes due to the fact that the gamma value is selected too large or too small, so that the scene adaptability of the algorithm is limited. Moreover, the algorithm calculated by adopting the e-exponential curve has high complexity and is not beneficial to engineering practice. In addition, in the construction of the remainder term, the prior art mostly performs the average distribution on all the occurring gray levels in the image, and even if the gray levels occur only twice, the remainder term is distributed, which reduces the contrast of the whole image in partial scenes. Therefore, a generalized histogram equalization algorithm which is simpler and more convenient to calculate and more reasonable in construction process is urgently needed in the field, and key parameters of the method can be selected in a self-adaptive mode so as to achieve better scene adaptability.
Disclosure of Invention
In view of the above, the present invention has been made to provide an infrared image adaptive enhancement method based on generalized histogram equalization that overcomes or at least partially solves the above-mentioned problems.
In order to solve the technical problem, the embodiment of the application discloses the following technical scheme:
the invention discloses an infrared image self-adaptive enhancement method based on generalized histogram equalization, which comprises the following steps:
s100, performing global point polishing on the input statistical histogram of the high dynamic range infrared image;
s200, calculating the local complexity and the overall complexity of the image at each pixel;
and S300, constructing a generalized histogram according to the calculated local complexity and the overall complexity, and carrying out equalization processing on the generalized histogram.
Further, the specific method of S100 is:
s101, constructing a gray level-pixel number statistical histogram of an input image;
s102, sequentially counting pixels of which the number of times of occurrence does not exceed 1,2, 3.. And max, and accumulating and summing the counted number of pixels until the total number of accumulated and summed pixels reaches a percentage threshold of the total number of pixels of the input image;
and S103, when the total number of the pixels of which the occurrence times are not more than n and the cumulative sum is greater than or equal to the percentage threshold of the total number of the pixels of the input image, regarding the gray level of which the occurrence times are not more than n as an invalid gray level, and regarding the rest as the valid gray level to perform polishing.
Further, in S200, the local complexity c (i, j) at each pixel is determined by calculating the variance of each pixel point in the image within the 3 × 3 local window.
Further, in S200, a laplacian-based method is used to evaluate the overall complexity σ of the image n The concrete formula is as follows:
where N denotes a filter template obtained by combining laplacian templates L1 and L2, W and H denote the width and height of an image, respectively, I (I, j) denotes an input image, and σ N denotes a parameter used in the present invention to evaluate the overall complexity of an image.
Further, in S300, a generalized histogram local complexity-fractional term mapping curve is used to calculate the fractional term r (i, j), and the specific formula is as follows:
wherein c (i, j) represents the local complexity of the pixel at the coordinate (i, j), th1 and th2 represent the upper limit and the lower limit of the threshold respectively, and r (i, j) represents the generalized histogram fraction term obtained after mapping, and the range is [0,1].
Further, the upper threshold th1 and the lower threshold th2 have the following ranges:
th1＝2*σ n
th2＝4*σ n
wherein σ n Representing the overall complexity of the image.
Further, in S300, the remainder is averagely distributed on the effective gray level after the global dotting, and a calculation formula for constructing the generalized histogram is as follows:
where I (I, j) represents the input image, δ (x, k) represents the kronecker function, taking 1 when x = k, and 0 for the rest; k represents the effective gray level after global dotting, n valid Representing the effective number of gray levels and h (k) the generalized histogram finally obtained.
Further, in S300, histogram equalization is performed on the generalized histogram to obtain a mapped gray level S (k), where the calculation formula of the mapped gray level S (k) is:
wherein h is sum Represents the sum of the pixels of the generalized histogram, and h (k) represents the generalized histogram finally obtained.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
1. the invention simplifies the traditional mapping curve of local complexity-fractional term of the generalized histogram based on the e index and provides the mapping curve based on linear transformation, so the calculation complexity is lower and the engineering practice is more facilitated.
2. According to the method, the upper and lower limit thresholds of the mapping curve of the generalized histogram local complexity-fractional term are calculated according to the parameter capable of representing the overall complexity of the image, so that the self-adaption can be realized for the images of different scenes.
3. The invention performs remainder term average distribution on the effective gray level obtained after global dotting, rather than distribution on all the appearing gray levels in the image, thereby effectively improving the overall contrast of partial scenes with few gray levels containing few pixels.
4. The processed output image can be directly used as a final output image to be displayed on common 8-bit display equipment, and is also suitable for basic layer processing in a Digital Detail Enhancement (DDE) algorithm based on a filtering layered framework, so that a better foundation is laid for the contrast of the final output image of the DDE algorithm.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of an infrared image adaptive enhancement method based on generalized histogram equalization in embodiment 1 of the present invention;
fig. 2 is a schematic diagram illustrating a comparison between a simplified mapping curve and a conventional mapping curve in embodiment 1 of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to solve the problems in the prior art, the embodiment of the invention provides an infrared image adaptive enhancement method based on generalized histogram equalization.
Example 1
The embodiment discloses an infrared image self-adaptive enhancement method based on generalized histogram equalization, which comprises the following steps:
s100, performing global point polishing on the input statistical histogram of the high dynamic range infrared image; in this embodiment, the specific method of S100 is:
s101, constructing a gray level-pixel number statistical histogram of an input image;
s102, sequentially counting pixels of which the number of times of occurrence does not exceed 1,2, 3.. And max, and accumulating and summing the counted number of pixels until the total number of the accumulated and summed pixels reaches a percentage threshold of the total number of the pixels of the input image, wherein in the embodiment, 0.1% of the total number of the pixels of the input image is preferably the percentage threshold;
and S103, when the total number of the pixels of which the occurrence times are not more than n and the cumulative sum is greater than or equal to the percentage threshold of the total number of the pixels of the input image, regarding the gray level of which the occurrence times are not more than n as an invalid gray level, and regarding the rest as the valid gray level to perform polishing.
S200, calculating the local complexity and the overall complexity of the image at each pixel; specifically, the present embodiment determines the local complexity c (i, j) at each pixel by calculating the variance of each pixel point in the image within a 3 × 3 local window.
In the embodiment, a method based on a Laplace template is adopted to evaluate the overall complexity sigma of the image n The concrete formula is as follows:
where N denotes a filter template obtained by combining laplacian templates L1 and L2, W and H denote the width and height of an image, respectively, I (I, j) denotes an input image, σ N denotes a parameter for evaluating the overall complexity of an image in the present invention, and σ N denotes a parameter for evaluating the overall complexity of an image n The smaller the size is, the smoother the whole image is, the smaller the noise is, and the smaller the complexity of the whole image is; the larger the σ n is, the larger the representative image overall complexity is.
And S300, constructing a generalized histogram according to the calculated local complexity and the overall complexity, and carrying out equalization processing on the generalized histogram.
Specifically, a generalized histogram local complexity-fractional term mapping curve is used to calculate a fractional term r (i, j), and the specific formula is as follows:
wherein c (i, j) represents the local complexity of the pixel at the coordinate (i, j), th1 and th2 represent the upper limit and the lower limit of the threshold respectively, and r (i, j) represents the generalized histogram fraction term obtained after mapping, and the range is [0,1].
A schematic diagram of a comparison between a mapping curve simplified by the method and a conventional mapping curve is shown in fig. 2. Th1 and th2 in the formula respectively represent a lower threshold limit and an upper threshold limit, and for the pixel with c < th1, the fractional term is set to be 0, so that the contribution of the fractional term to the histogram is weakened, and the background noise is suppressed as much as possible; for the pixels with c > th2, the pixels are considered to contribute more to the histogram, so the fractional term is set to 1. For pixels th1< c < th2, it is assumed that the larger c, the closer the contribution to the histogram approaches 1. Theoretically, the values of th1 and th2 are proportional to the scene complexity, so in the invention, the values of th1 and th2 are calculated by using σ n in a self-adaptive manner:
th1＝2*σ n
th2＝4*σ n
wherein σ n Representing the overall complexity of the image.
Carrying out average distribution of remainder on the effective gray level after global dotting, and constructing a generalized histogram by the following calculation formula:
where I (I, j) represents the input image, δ (x, k) represents the kronecker function, taking 1 when x = k, and 0 for the rest; k represents the effective gray level after global dotting, n valid Representing the effective number of gray levels and h (k) the generalized histogram finally obtained.
Performing histogram equalization processing on the generalized histogram to obtain a mapped gray level s (k), wherein a calculation formula of the mapped gray level s (k) is as follows:
wherein h is sum Represents the sum of the pixels of the generalized histogram, and h (k) represents the generalized histogram finally obtained.
The infrared image self-adaptive enhancement method based on generalized histogram equalization disclosed by the embodiment simplifies the traditional generalized histogram local complexity-fractional term mapping curve based on the e index, and provides a mapping curve based on linear transformation, so that the calculation complexity is lower, and the method is more beneficial to engineering practice. According to the parameters capable of representing the overall complexity of the image, provided by the invention, the upper and lower limit thresholds of the mapping curve of the local complexity-fractional term of the generalized histogram are calculated, so that the self-adaption can be realized for the images of different scenes. The invention performs remainder term average distribution on the effective gray level obtained after global dotting, rather than distribution on all the appearing gray levels in the image, thereby effectively improving the overall contrast of partial scenes with few gray levels containing few pixels. The output image processed by the invention not only can be directly used as the final output image to be displayed on the common 8-bit display equipment, but also is suitable for the basic layer processing in the digital detail enhancement algorithm based on the filtering layered framework, and lays a better foundation for the contrast of the final output image of the DDE algorithm.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Claims (4)
1. An infrared image self-adaptive enhancement method based on generalized histogram equalization is characterized by comprising the following steps:
s100, performing global point polishing on the input statistical histogram of the high dynamic range infrared image;
s200, calculating the local complexity and the overall complexity of the image at each pixel; in S200, determining local complexity c (i, j) of each pixel by calculating the variance of each pixel point in the image in a 3 × 3 local window; in S200, a method based on a Laplace template is adopted to evaluate the overall complexity sigma of the image n The concrete formula is as follows:
wherein, N represents a filtering template obtained by combining laplacian templates L1 and L2, W and H represent the width and height of an image respectively, I (I, j) represents an input image, and σ N represents a parameter for evaluating the overall complexity of the image in the present invention;
s300, constructing a generalized histogram according to the calculated local complexity and the overall complexity, and carrying out equalization processing on the generalized histogram; in S300, a generalized histogram local complexity-fractional term mapping curve is used to calculate a fractional term r (i, j), and the specific formula is as follows:
wherein c (i, j) represents the local complexity of the pixel at the coordinate (i, j), th1 and th2 represent the upper limit and the lower limit of the threshold respectively, r (i, j) represents the generalized histogram fractional term obtained after mapping, and the range is [0,1]; the upper threshold th1 and the lower threshold th2 have the following ranges:
th1＝2*σ n
th2＝4*σ n
wherein σ n Representing the overall complexity of the image.
2. The infrared image adaptive enhancement method based on generalized histogram equalization according to claim 1, wherein the specific method of S100 is:
s101, constructing a gray level-pixel number statistical histogram of an input image;
s102, sequentially counting pixels of which the number of times of occurrence does not exceed 1,2, 3.. And max, and accumulating and summing the counted number of pixels until the total number of accumulated and summed pixels reaches a percentage threshold of the total number of pixels of the input image;
and S103, when the total number of the pixels of which the occurrence times are not more than n and the cumulative sum is greater than or equal to the percentage threshold of the total number of the pixels of the input image, regarding the gray level of which the occurrence times are not more than n as an invalid gray level, and regarding the rest as the valid gray level to perform polishing.
3. The infrared image self-adaptive enhancement method based on generalized histogram equalization as claimed in claim 1, wherein in S300, the average distribution of remainder is performed on the effective gray level after global dotting, and the calculation formula for constructing the generalized histogram is:
where I (I, j) represents the input image, δ (x, k) represents the kronecker function, taking 1 when x = k, and 0 for the rest; k represents the effective gray level after global dotting, n valid Representing the effective number of gray levels and h (k) the generalized histogram finally obtained.
4. The infrared image adaptive enhancement method based on generalized histogram equalization as claimed in claim 1, wherein in S300, histogram equalization is performed on the generalized histogram to obtain a mapped gray level S (k), wherein the calculation formula of the mapped gray level S (k) is:
wherein h is sum Represents the sum of the pixels of the generalized histogram, and h (k) represents the generalized histogram finally obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110108751.5A CN112837250B (en) | 2021-01-27 | 2021-01-27 | Infrared image self-adaptive enhancement method based on generalized histogram equalization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110108751.5A CN112837250B (en) | 2021-01-27 | 2021-01-27 | Infrared image self-adaptive enhancement method based on generalized histogram equalization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112837250A CN112837250A (en) | 2021-05-25 |
| CN112837250B true CN112837250B (en) | 2023-03-10 |
Family
ID=75931824
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110108751.5A Active CN112837250B (en) | 2021-01-27 | 2021-01-27 | Infrared image self-adaptive enhancement method based on generalized histogram equalization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112837250B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117078568B (en) * | 2023-10-12 | 2024-02-23 | 成都智明达电子股份有限公司 | Infrared image enhancement method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2279359A1 (en) * | 1999-07-30 | 2001-01-30 | Basantkumar John Oommen | A method of generating attribute cardinality maps |
| CN105898495A (en) * | 2016-05-26 | 2016-08-24 | 维沃移动通信有限公司 | Method for pushing mobile terminal recommended information and mobile terminal |
| CN110211056A (en) * | 2019-05-06 | 2019-09-06 | 南京理工大学 | Adaptive infrared image based on value histogram in part removes striped algorithm |
| CN110827229A (en) * | 2019-11-01 | 2020-02-21 | 中国科学院长春光学精密机械与物理研究所 | Infrared image enhancement method based on texture weighted histogram equalization |
| CN111899205A (en) * | 2020-08-10 | 2020-11-06 | 国科天成（北京）科技有限公司 | Image enhancement method of scene self-adaptive wide dynamic infrared thermal imaging |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9900478B2 (en) * | 2003-09-04 | 2018-02-20 | Flir Systems, Inc. | Device attachment with infrared imaging sensor |
| US9307162B2 (en) * | 2014-05-21 | 2016-04-05 | Himax Imaging Limited | Local enhancement apparatus and method to generate high dynamic range images by blending brightness-preserved and brightness-adjusted blocks |
| CN105469373B (en) * | 2014-09-04 | 2018-07-31 | 南京理工大学 | A kind of Retinex image enchancing methods and system |
| CN111161163B (en) * | 2019-12-05 | 2023-03-07 | 华侨大学 | Infrared image contrast enhancement method for histogram double gamma correction |
-
2021
- 2021-01-27 CN CN202110108751.5A patent/CN112837250B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2279359A1 (en) * | 1999-07-30 | 2001-01-30 | Basantkumar John Oommen | A method of generating attribute cardinality maps |
| CN105898495A (en) * | 2016-05-26 | 2016-08-24 | 维沃移动通信有限公司 | Method for pushing mobile terminal recommended information and mobile terminal |
| CN110211056A (en) * | 2019-05-06 | 2019-09-06 | 南京理工大学 | Adaptive infrared image based on value histogram in part removes striped algorithm |
| CN110827229A (en) * | 2019-11-01 | 2020-02-21 | 中国科学院长春光学精密机械与物理研究所 | Infrared image enhancement method based on texture weighted histogram equalization |
| CN111899205A (en) * | 2020-08-10 | 2020-11-06 | 国科天成（北京）科技有限公司 | Image enhancement method of scene self-adaptive wide dynamic infrared thermal imaging |
Non-Patent Citations (3)
| Title |
|---|
| Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction;MinjieWan et al.;《remote sensing》;20180427;第1-34页 * |
| 海面红外图像的动态范围压缩及细节增强;王园园;《红外与激光工程》;20190131;第48卷(第1期);第0126003-1-0126003-9页 * |
| 高动态范围红外图像的显示与细节增强;单瑞卿等;《光学技术》;20190430;第45卷(第4期);第475-481页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112837250A (en) | 2021-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lee et al. | Contrast enhancement based on layered difference representation of 2D histograms | |
| CN108537756B (en) | Single image defogging method based on image fusion | |
| CN107451969A (en) | Image processing method, device, mobile terminal and computer-readable recording medium | |
| US20140079319A1 (en) | Methods for enhancing images and apparatuses using the same | |
| EP2202684A1 (en) | Image processing device and program | |
| CN109686342B (en) | Image processing method and device | |
| CN107509031A (en) | Image processing method, device, mobile terminal and computer-readable recording medium | |
| CN109671035B (en) | Infrared image enhancement method based on histogram | |
| CN109064418A (en) | A kind of Images Corrupted by Non-uniform Noise denoising method based on non-local mean | |
| CN111127343B (en) | Histogram double-control infrared image contrast enhancement method | |
| CN106557729B (en) | Apparatus and method for processing face image | |
| CN112837250B (en) | Infrared image self-adaptive enhancement method based on generalized histogram equalization | |
| WO2020164266A1 (en) | Living body detection method and system, and terminal device | |
| CN113810611B (en) | Data simulation method and device for event camera | |
| CN116363021A (en) | Intelligent collection system for nursing and evaluating wound patients | |
| CN115439384A (en) | Ghost-free multi-exposure image fusion method and device | |
| CN113689373A (en) | Image processing method, device, equipment and computer readable storage medium | |
| CN112102348A (en) | Image processing apparatus | |
| CN112507818A (en) | Illumination estimation method and system based on near-infrared image | |
| CN116630198A (en) | Multi-scale fusion underwater image enhancement method combining self-adaptive gamma correction | |
| CN112435188B (en) | JND prediction method and device based on direction weight, computer equipment and storage medium | |
| US11317069B2 (en) | Image processing method based on sensor characteristics | |
| CN114764771A (en) | Image quality evaluation method, device, equipment, chip and storage medium | |
| CN107404600A (en) | Image processing apparatus and method | |
| CN118097160B (en) | Critical critical illness state monitoring system based on vision technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |