CN111161162B - Processing method and device for infrared image detail layer - Google Patents
Processing method and device for infrared image detail layer Download PDFInfo
- Publication number
- CN111161162B CN111161162B CN201911237672.3A CN201911237672A CN111161162B CN 111161162 B CN111161162 B CN 111161162B CN 201911237672 A CN201911237672 A CN 201911237672A CN 111161162 B CN111161162 B CN 111161162B
- Authority
- CN
- China
- Prior art keywords
- mask function
- noise mask
- original
- detail layer
- image
- 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
- 238000003672 processing method Methods 0.000 title abstract description 6
- 238000000034 method Methods 0.000 claims abstract description 49
- 238000012545 processing Methods 0.000 claims abstract description 30
- 238000001914 filtration Methods 0.000 claims abstract description 21
- 230000000873 masking effect Effects 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 8
- 238000005457 optimization Methods 0.000 claims description 3
- 230000003321 amplification Effects 0.000 abstract description 15
- 238000003199 nucleic acid amplification method Methods 0.000 abstract description 15
- 230000001629 suppression Effects 0.000 abstract description 9
- 238000004364 calculation method Methods 0.000 abstract description 7
- 230000006870 function Effects 0.000 description 124
- 230000008569 process Effects 0.000 description 7
- 230000002146 bilateral effect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000007704 transition Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000003707 image sharpening Methods 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000001931 thermography Methods 0.000 description 1
Images
Classifications
-
- G06T5/70—
-
- 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
Abstract
The invention discloses a processing method and a device of an infrared image detail layer, wherein the method comprises the following steps: acquiring an original infrared image; performing guide filtering on the original infrared image to obtain an original detail layer image; acquiring a first noise mask function according to the original infrared image; acquiring a second noise mask function according to the original detail layer image; obtaining a third noise mask function according to the first noise mask function and the second noise mask function; and processing the original detail layer image according to the third noise mask function to obtain an optimized detail layer image. The invention reduces the calculation complexity and realizes the mutual balance between detail amplification and noise suppression of the detail layer image.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a method and a device for processing an infrared image detail layer.
Background
With the rapid development of scientific technology, the infrared thermal imaging technology has made great progress. In recent years, in order to obtain more accurate infrared radiation intensity and improve infrared image quality, sampling accuracy of an ADC (Analog-to-Digital Converter) is greatly improved, the number of conversion bits reaches 14 bits or more, and a Digital image signal after sampling can reach a dynamic range of 0 to 16383, that is, a so-called high dynamic range. Compared with the previous infrared image, the High-Dynamic Range (HDR) infrared image can provide more detail texture information, and the definition and the human eye perception of the image are improved by improving the contrast and the detail of an interested area in the HDR infrared image and inhibiting or even eliminating other regions and noises which are not concerned in the image.
In 2005, with the introduction of Digital Detail Enhancement (DDE) technology of the FLIR corporation in the united states, the infrared image Enhancement method based on filtering hierarchy became a research hotspot of scholars at home and abroad. The basic idea is to carry out low-pass filtering on an original image to obtain a basic layer image containing low-frequency information such as an image background and a detail layer image containing high-frequency information such as image details, textures and noise, then respectively process the basic layer image and the detail layer image, and finally fuse the processed basic layer image and the detail layer image to obtain a final output image. For HDR infrared images, the processing of detail layers tends to be more important, and is also more difficult than the base layer because the noise and weak details of the detail layers are difficult to distinguish in the spatial domain. In an early infrared image enhancement method based on bilateral filtering layering, parameters of Gamma transform (Gamma transform) or a bilateral filter are adopted to process a detail layer image, however, the Gamma transform is not suitable for distribution characteristics of the infrared image, a fixed Gamma value cannot be suitable for different infrared scenes, and a gradient inversion phenomenon exists at the edge of the bilateral filter. In 2010, the gradient inversion problem of the bilateral filter was well solved by the presence of the bootstrap filter. The guide filter is a linear filter, is simple to operate and is better applied to various image processing aspects (such as image sharpening, image fusion, image defogging and the like). When the guide image is an input image, the guide filter has the characteristic of edge-preserving and denoising like a bilateral filter, so that the guide filter is well applied to the aspect of image detail enhancement. Liu et al proposed a Guided Filter and Digital Detail Enhancement (GF & DDE) based infrared image Detail Enhancement algorithm and obtained a better Enhancement effect in 2014, and then the Guided Filter layering based infrared image Detail Enhancement method became the mainstream development trend of the Filter layering method. The GF & DDE method adopts the kernel coefficient of the guide filter as the gain factor of a detail layer, but the kernel coefficient of the guide filter needs to be independently calculated, and the calculation is more complex; in 2015, f.garcia amplified the pilot filter coefficients as gain factors of detail layers for the first time in an article that proposed the TDDE-LAGC algorithm, so as not to increase the amount of computation. But it is considered that directly using the guided filter coefficients results in masking noise while attenuating some details. In the TDDE2-LAGC method proposed by the same year, two guide filter coefficients are multiplied by a noise mask function of a detail layer to process the obtained detail layer image, and the processing result well suppresses noise, but simultaneously blurs details.
In summary, the current method for processing the detail layer of the infrared image not only has high computational complexity, but also has difficulty in achieving mutual balance between detail amplification and noise suppression.
Disclosure of Invention
In view of the above problems, the present invention provides a method and an apparatus for processing a detail layer of an infrared image, which achieve mutual balance between detail amplification and noise suppression of the detail layer image while reducing the computational complexity.
In a first aspect, the present application provides the following technical solutions through an embodiment:
a method of processing an infrared image detail layer, the method comprising:
acquiring an original infrared image;
performing guide filtering on the original infrared image to obtain an original detail layer image;
acquiring a first noise mask function according to the original infrared image;
acquiring a second noise mask function according to the original detail layer image;
obtaining a third noise mask function according to the first noise mask function and the second noise mask function;
and processing the original detail layer image according to the third noise mask function to obtain an optimized detail layer image.
Preferably, the performing guided filtering on the original infrared image to obtain an original detail layer image includes:
guiding and filtering the original infrared image to obtain a base layer image;
and calculating the difference between the original infrared image and the base layer image to obtain an original detail layer image.
Preferably, the obtaining a third noise mask function according to the first noise mask function and the second noise mask function includes:
and obtaining a third noise mask function according to the sum of the first noise mask function and the second noise mask function.
Preferably, the obtaining a third noise mask function according to the first noise mask function and the second noise mask function includes:
based on
Obtaining the third noise mask function; wherein, M k As the third noise mask function, the first noise mask function is
The second noise mask function is
σ k1 2 And σ k2 2 The gray level variance, epsilon, of the pixels in the original infrared image and the original detail layer image respectively 1 And ε 2 Are respectively M k1 And M k2 A penalty value of.
Preferably, the calculation window size of the gray level variance of the pixels in the original infrared image and the original detail layer image is 3 × 3.
Preferably, the obtaining a third noise mask function according to the first noise mask function and the second noise mask function includes:
assigning a first weight to the first noise mask function;
assigning a second weight to the second noise mask function; wherein the sum of the first weight and the second weight is 1;
and obtaining the third noise mask function according to the sum of the first noise mask function assigned with the first weight and the second noise mask function assigned with the second weight.
In a second aspect, based on the same inventive concept, the present application provides the following technical solutions through an embodiment:
an apparatus for processing an infrared image detail layer, the apparatus comprising:
the original infrared image acquisition module is used for acquiring an original infrared image;
the original detail layer image acquisition module is used for guiding and filtering the original infrared image to obtain an original detail layer image;
the first noise mask function acquisition module is used for acquiring a first noise mask function according to the original infrared image;
the second noise mask function acquisition module is used for acquiring a second noise mask function according to the original detail layer image;
a third noise mask function obtaining module, configured to obtain a third noise mask function according to the first noise mask function and the second noise mask function;
and the detail layer image optimization module is used for processing the original detail layer image according to the third noise mask function to obtain an optimized detail layer image.
Preferably, the third noise mask function obtaining module is further specifically configured to:
and obtaining a third noise mask function according to the sum of the first noise mask function and the second noise mask function.
Preferably, the third noise mask function obtaining module is further specifically configured to:
based on
Obtaining the third noise mask function; wherein M is k As the third noise mask function, the first noise mask function is
The second noise mask function is
σ k1 2 And σ k2 2 The gray level variance, epsilon, of the pixels in the original infrared image and the original detail layer image respectively 1 And epsilon 2 Are respectively M k1 And M k2 A penalty value of.
In a third aspect, based on the same inventive concept, the present application provides the following technical solutions through an embodiment:
a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of the first aspects.
The embodiment of the invention provides a processing method and a device of an infrared image detail layer, wherein the method comprises the steps of obtaining an original infrared image; performing guide filtering on the original infrared image to obtain an original detail layer image; acquiring a first noise mask function according to the original infrared image; acquiring a second noise mask function according to the original detail layer image; obtaining a third noise mask function according to the first noise mask function and the second noise mask function; the third noise mask function obtained by integrating the first noise mask function and the second noise mask function can ensure that the balance between noise suppression and detail amplification is realized when the detail layer image is processed. And finally, processing the original detail layer image according to a third noise mask function, and obtaining an optimized detail layer image which is a better detail layer image. The method of the embodiment of the invention reduces the computational complexity and realizes the mutual balance between the detail amplification and the noise suppression of the detail layer image.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various additional advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a method for processing an infrared image detail layer according to a first embodiment of the present invention;
FIG. 2 is a diagram illustrating a detail layer image processed by a first noise mask function according to a first embodiment of the present invention;
FIG. 3 is a diagram illustrating a detail layer image processed by a second noise mask function according to a first embodiment of the present invention
FIG. 4 is a diagram illustrating an optimized detail layer image processed by a third noise mask function according to a first embodiment of the present invention
Fig. 5 shows a functional block diagram of an infrared image detail layer processing apparatus according to a second embodiment 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 by 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.
First embodiment
Referring to fig. 1, a flowchart of a method for processing an infrared image detail layer according to a first embodiment of the present invention is shown, where the method includes:
step S10: acquiring an original infrared image;
step S20: performing guide filtering on the original infrared image to obtain an original detail layer image;
step S30: acquiring a first noise mask function according to the original infrared image;
step S40: acquiring a second noise mask function according to the original detail layer image;
step S50: obtaining a third noise mask function according to the first noise mask function and the second noise mask function;
step S60: and processing the original detail layer image according to the third noise mask function to obtain an optimized detail layer image.
In step S10, if the method is applied to a bottom layer of an electronic device, a system, or application software installed in the system, the original infrared image may be an infrared image captured by the electronic device, or an infrared image imported by a user from another device.
Step S20: and performing guide filtering on the original infrared image to obtain an original detail layer image.
In step S20, the method specifically includes:
step S21: guiding and filtering the original infrared image to obtain a base layer image;
step S22: and calculating the difference between the original infrared image and the base layer image to obtain an original detail layer image.
Specifically, the original infrared image is subjected to guiding filtering, the filtering output serves as a base layer image, and then the base layer image is subtracted from the original infrared image to obtain an original detail layer image.
Step S30: and acquiring a first noise mask function according to the original infrared image.
Step S40: acquiring a second noise mask function according to the original detail layer image;
in steps S30 to S40, the order of implementation is not limited. The specific acquisition mode is as follows: the first noise masking function is
The second noise mask function is
σ k1 2 And σ k2 2 The gray level variance, epsilon, of the corresponding pixels in the original infrared image and the detail layer image respectively 1 And ε 2 Are each M k1 And M k2 A penalty value of.
Step S50: and obtaining a third noise mask function according to the first noise mask function and the second noise mask function.
In step S50, the present embodiment provides two specific embodiments:
firstly, the method comprises the following steps:
step S51a: and obtaining a third noise mask function according to the sum of the first noise mask function and the second noise mask function.
More specifically, the third noise mask function may be calculated by the following formula: based on
Obtaining a third noise mask function; wherein M is k As the third noise mask function, the first noise mask function is
The second noise mask function is
σ k1 2 And σ k2 2 The gray level variance, epsilon, of the corresponding pixels in the original infrared image and the original detail layer image respectively 1 And ε 2 Are respectively M k1 And M k2 A penalty value of.
In order to better process the original detail layer image in this embodiment, the gray-scale variance calculation window size of the pixels in the original infrared image and the original detail layer image needs to be controlled to 3 × 3. The window size is controlled to be 3 x 3 mainly to reflect the spatial local pixel distribution characteristics of each pixel point, if the window is too large, more pixels can be introduced when the variance is calculated, the local characteristics cannot be well reflected, and meanwhile, the calculation amount is also increased.
Secondly, the method comprises the following steps:
step S51b: assigning a first weight to the first noise mask function;
step S52b: assigning a second weight to the second noise mask function; wherein the sum of the first weight and the second weight is 1;
step S53b: obtaining the third noise mask function according to a sum of the first noise mask function assigned the first weight and the second noise mask function assigned the second weight.
In particular, in this embodiment the third noise masking function M k Can be expressed as: m k ＝w 1 *M k1 +w 2 *M k2 Wherein w is 1 、w 2 Respectively a first weight and a second weight, w 1 +w 2 And =1, by performing weight distribution on the first noise mask function and the second noise mask function, more fine adjustment can be performed on the processing of the infrared image of the detail layer, and it is ensured that an optimal balance point is found.
Step S60: and processing the original detail layer image according to the third noise mask function to obtain an optimized detail layer image.
In step S50, the calculation may be specifically performed as follows:
I dp (i,j)＝G*M k *I d (i,j)；
wherein, I dp (I, j) is the optimized detail layer image finally obtained, (I, j) is the horizontal and vertical coordinates of the pixel, G is the amplification factor selected manually, and I d (i, j) is the first step to get the original detail layer image of the pixel (i, j) before processing. Further, in the present embodiment, the noise mask function M of the original infrared image k1 Penalty term ε in 1 The noise masking function M of the detail layer image is the same as the corresponding parameter ε of the guiding filter used k2 Penalty term of (2) 2 The value is 50-60, preferably 55, and the value is too large, so that the image is too smooth, and the texture and the detail are weakened; if the value is too small, the noise will be amplified, and in other specific embodiments of this embodiment, manual adjustment and modification may be performed according to different scene images. The value of the manually selected amplification factor G in the final detail layer processing method can be 2.3-2.7, and the advantages are goodThe selected value is 2.5, so that the processing effect on the detail layer image is better, and the image noise is obviously enhanced due to overlarge value; if the value is too small, weak details and textures in the image cannot be effectively enhanced.
The detail layer image processed by the method in the embodiment can realize mutual balance between detail amplification and noise suppression with low computational complexity, and obtain a better detail layer image. In order to make the effect of the method of the present embodiment more obvious, a specific example is illustrated below.
1. Using a first noise mask function M calculated from the original infrared image k1 The detail layer image is processed as follows:
I 1,dp (i,j)＝G*M k1 *I d (i,j)
wherein, (I, j) is the horizontal and vertical coordinates of the pixel, G is a manually selected amplification factor, I d (I, j) obtaining a pre-processed detail layer image in a first step, I 1,dp (i, j) is the processed detail layer image, specifically M k1 Penalty term ε in 1 Same as the corresponding parameter ε of the guiding filter used, M k2 Penalty term ε in 2 The value is 55, and the value of the amplification factor G is 2.5, which is still adopted in the subsequent description of this example and is not described again. Referring to FIG. 2, it can be seen that the variance of the original IR image results in a first noise masking function M k1 The noise of the homogeneous region is suppressed while the weak details and texture are magnified (see image details shown in boxes 2 and 3 in fig. 2), but the noise of the transition region is also magnified (see image details shown in box 1 in fig. 2).
2. Using a second noise mask function M calculated for the original detail layer k2 The detail layer image is processed as follows:
I 2,dp (i,j)＝G*M k2 *I d (i,j)
wherein, (I, j) is the horizontal and vertical coordinates of the pixel, G is a manually selected amplification factor, I d (I, j) obtaining a pre-processed detail layer image in a first step, I 2,dp And (i, j) is the processed detail layer image. Please refer toTurning to FIG. 3, it can be seen that a second noise mask function M is obtained using the variance of the detail layer image k2 The noise of the transition region and the noise of the uniform region can be effectively suppressed (see the image details shown in the box No. 1 in fig. 3), but the method weakens the weak details and the texture (see the image details shown in the boxes No. 2 and 3 in fig. 3) compared with the former method.
3. The third noise mask function M in the processing method of the infrared image detail layer in the embodiment is adopted k The detail layer image is processed as follows:
I dp (i,j)＝G*M k *I d (i,j)
wherein, (I, j) is the horizontal and vertical coordinates of the pixel, G is a manually selected amplification factor, I d (I, j) obtaining a pre-processed detail layer image in a first step, I dp (i, j) is the processed detail layer image (i.e., the optimized detail layer image). Referring to FIG. 4, it can be seen that the third noise masking function M is utilized k The noise of the transition region and the noise of the uniform region can be effectively suppressed (see the image details shown in the block 1 in fig. 4), meanwhile, the weak details and the texture are also amplified to some extent (see the image details shown in the blocks 2 and 3 in fig. 4), and the balance between noise suppression and detail amplification is realized under the condition of low computational complexity.
The method for processing an infrared image detail layer provided in this embodiment includes: acquiring an original infrared image; performing guide filtering on the original infrared image to obtain an original detail layer image; acquiring a first noise mask function according to the original infrared image; acquiring a second noise mask function according to the original detail layer image; obtaining a third noise mask function according to the first noise mask function and the second noise mask function; the third noise mask function obtained by integrating the first noise mask function and the second noise mask function can ensure that the balance between noise suppression and detail amplification is realized when the detail layer image is processed. And finally, processing the original detail layer image according to a third noise mask function, and obtaining an optimized detail layer image which is a better detail layer image. The method reduces the calculation complexity and simultaneously realizes the mutual balance between the detail amplification and the noise suppression of the detail layer image.
Second embodiment
Referring to fig. 5, a second embodiment of the invention provides an apparatus 300 for processing infrared image detail layers based on the same inventive concept. Fig. 5 shows a functional block diagram of an apparatus 300 for processing an infrared image detail layer according to a second embodiment of the present invention.
The apparatus 300 comprises:
an original infrared image acquisition module 301, configured to acquire an original infrared image;
an original detail layer image obtaining module 302, configured to perform guided filtering on the original infrared image to obtain an original detail layer image;
a first noise mask function obtaining module 303, configured to obtain a first noise mask function according to the original infrared image;
a second noise mask function obtaining module 304, configured to obtain a second noise mask function according to the original detail layer image;
a third noise mask function obtaining module 305, configured to obtain a third noise mask function according to the first noise mask function and the second noise mask function;
and a detail layer image optimization module 306, configured to process the original detail layer image according to the third noise mask function, to obtain an optimized detail layer image.
As an optional implementation manner, the original detail layer image obtaining module 302 is specifically configured to:
performing guide filtering on the original infrared image to obtain a base layer image;
and calculating the difference between the original infrared image and the base layer image to obtain an original detail layer image.
As an optional implementation manner, the third noise mask function obtaining module 305 is further specifically configured to:
and obtaining a third noise mask function according to the sum of the first noise mask function and the second noise mask function.
As an optional implementation manner, the third noise mask function obtaining module 305 is further specifically configured to:
based on
Obtaining the third noise mask function; wherein M is k As the third noise mask function, the first noise mask function is
The second noise mask function is
σ k1 2 And σ k2 2 The gray level variance, epsilon, of the pixels corresponding to windows of 3 x 3 size in the original infrared image and the original detail layer image respectively 1 And epsilon 2 Are respectively M k1 And M k2 A penalty value of.
It should be noted that, the implementation and technical effects of the apparatus 300 for processing an infrared image detail layer according to the embodiment of the present invention are the same as those of the foregoing method embodiment, and for brevity, reference may be made to the corresponding contents in the foregoing method embodiment where no part of the apparatus embodiment is mentioned.
The device-integrated functional modules provided by the present invention may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, all or part of the flow of the method of implementing the above embodiments may also be implemented by a computer program, which may be stored in a computer readable storage medium and used by a processor to implement the steps of the above embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the devices in an embodiment may be adaptively changed and arranged in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of a gateway, proxy server, system in accordance with embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website, or provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (5)
1. A method of processing an infrared image detail layer, the method comprising:
acquiring an original infrared image;
performing guiding filtering on the original infrared image to obtain an original detail layer image;
acquiring a first noise mask function according to the original infrared image;
acquiring a second noise mask function according to the original detail layer image;
obtaining a third noise mask function according to the first noise mask function and the second noise mask function;
processing the original detail layer image according to the third noise mask function to obtain an optimized detail layer image;
wherein the said according to the said secondA noise masking function and the second noise masking function to obtain a third noise masking function, comprising: based on
Obtaining the third noise mask function; wherein, M k As the third noise mask function, the first noise mask function is
The second noise mask function is
σ k1 2 And σ k2 2 The gray level variance, epsilon, of the pixels in the original infrared image and the original detail layer image, respectively 1 And ε 2 Are respectively M k1 And M k2 A penalty value of (d); or, the obtaining a third noise mask function according to the first noise mask function and the second noise mask function includes: assigning a first weight to the first noise mask function; assigning a second weight to the second noise mask function; wherein the sum of the first weight and the second weight is 1; obtaining the third noise mask function according to a sum of the first noise mask function assigned the first weight and the second noise mask function assigned the second weight.
2. The method of claim 1, wherein the guided filtering of the original infrared image to obtain an original detail layer image comprises:
guiding and filtering the original infrared image to obtain a base layer image;
and calculating the difference between the original infrared image and the base layer image to obtain an original detail layer image.
3. The method of claim 1, wherein the window size for calculating the gray scale variance of pixels in the original infrared image and the original detail layer image is 3 x 3.
4. An apparatus for processing an infrared image detail layer, the apparatus comprising:
the original infrared image acquisition module is used for acquiring an original infrared image;
the original detail layer image acquisition module is used for guiding and filtering the original infrared image to obtain an original detail layer image;
the first noise mask function acquisition module is used for acquiring a first noise mask function according to the original infrared image;
the second noise mask function acquisition module is used for acquiring a second noise mask function according to the original detail layer image;
a third noise mask function obtaining module, configured to obtain a third noise mask function according to the first noise mask function and the second noise mask function;
the detail layer image optimization module is used for processing the original detail layer image according to the third noise mask function to obtain an optimized detail layer image;
a third noise mask function acquisition module, in particular for use on the basis of
Obtaining the third noise mask function; wherein M is k As the third noise mask function, the first noise mask function is
The second noise mask function is
σ k1 2 And σ k2 2 The gray level variance, epsilon, of the pixels in the original infrared image and the original detail layer image respectively 1 And ε 2 Are respectively asM k1 And M k2 A penalty value of (d); or, in particular, to assign a first weight to the first noise mask function; assigning a second weight to the second noise mask function; wherein the sum of the first weight and the second weight is 1; and obtaining the third noise mask function according to the sum of the first noise mask function assigned with the first weight and the second noise mask function assigned with the second weight.
5. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911237672.3A CN111161162B (en) | 2019-12-06 | 2019-12-06 | Processing method and device for infrared image detail layer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911237672.3A CN111161162B (en) | 2019-12-06 | 2019-12-06 | Processing method and device for infrared image detail layer |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111161162A CN111161162A (en) | 2020-05-15 |
| CN111161162B true CN111161162B (en) | 2022-10-14 |
Family
ID=70556413
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911237672.3A Active CN111161162B (en) | 2019-12-06 | 2019-12-06 | Processing method and device for infrared image detail layer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111161162B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20220010264A (en) * | 2020-07-17 | 2022-01-25 | 에스케이하이닉스 주식회사 | Noise removing circuit, image sensing device and operation method thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787902A (en) * | 2016-03-22 | 2016-07-20 | 天津大学 | Image noise reduction method which utilizes partitioning ordering to detect noise |
| CN107408296A (en) * | 2015-03-05 | 2017-11-28 | Iee国际电子工程股份公司 | Real-time noise for high dynamic range images eliminates and the method and system of image enhaucament |
| CN108470325A (en) * | 2018-02-08 | 2018-08-31 | 北京空间飞行器总体设计部 | A kind of face battle array stares the identification of infrared remote sensing image space-time three-dimensional noise and compensation method |
-
2019
- 2019-12-06 CN CN201911237672.3A patent/CN111161162B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107408296A (en) * | 2015-03-05 | 2017-11-28 | Iee国际电子工程股份公司 | Real-time noise for high dynamic range images eliminates and the method and system of image enhaucament |
| CN105787902A (en) * | 2016-03-22 | 2016-07-20 | 天津大学 | Image noise reduction method which utilizes partitioning ordering to detect noise |
| CN108470325A (en) * | 2018-02-08 | 2018-08-31 | 北京空间飞行器总体设计部 | A kind of face battle array stares the identification of infrared remote sensing image space-time three-dimensional noise and compensation method |
Non-Patent Citations (2)
| Title |
|---|
| Noise Removal and Real-Time Detail Enhancement of High-Dynamic-Range Infrared Images with Time Consistency;Frederic Garcia et al.;《Conference International Conference on Quality Control by Artificial Vision》;20150630;1-10 * |
| 基于滤波分层的红外图像细节增强算法研究;樊启明;《中国优秀硕士学位论文全文数据库 信息科技辑》;20190315;I135-101 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111161162A (en) | 2020-05-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Al-Ameen et al. | An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization | |
| Xu et al. | A novel multi-scale fusion framework for detail-preserving low-light image enhancement | |
| Zhou et al. | Retinex-based laplacian pyramid method for image defogging | |
| Lee et al. | Structure‐texture decomposition of images with interval gradient | |
| CN108090886B (en) | High dynamic range infrared image display and detail enhancement method | |
| CN109191387B (en) | Infrared image denoising method based on Butterworth filter | |
| Lee et al. | Noise reduction and adaptive contrast enhancement for local tone mapping | |
| Pal et al. | Rician noise removal in magnitude MRI images using efficient anisotropic diffusion filtering | |
| Rahman et al. | Structure revealing of low-light images using wavelet transform based on fractional-order denoising and multiscale decomposition | |
| Nnolim | Smoothing and enhancement algorithms for underwater images based on partial differential equations | |
| Ambalathankandy et al. | An adaptive global and local tone mapping algorithm implemented on FPGA | |
| Zhou et al. | A multifeature fusion method for the color distortion and low contrast of underwater images | |
| CN111161162B (en) | Processing method and device for infrared image detail layer | |
| Mu et al. | Low and non-uniform illumination color image enhancement using weighted guided image filtering | |
| CN113808036A (en) | Low-illumination image enhancement and denoising method based on Retinex model | |
| Qu et al. | An improved algorithm of multi-exposure image fusion by detail enhancement | |
| CN110648302B (en) | Light field full-focus image fusion method based on edge enhancement guide filtering | |
| Hayat et al. | Detailed and enhanced multi-exposure image fusion using recursive filter | |
| Xu et al. | A novel variational model for detail-preserving low-illumination image enhancement | |
| Borah et al. | A GPU-accelerated modified unsharp-masking method for high-frequency background-noise suppression | |
| CN111489319A (en) | Infrared image enhancement method based on multi-scale bilateral filtering and visual saliency | |
| CN114202476A (en) | Infrared image enhancement method, device, equipment and computer readable medium | |
| KR101877808B1 (en) | Image contrast enhancement method and apparatus using local histogram equalization based on multiple layters overlapped block | |
| CN109035228A (en) | A kind of radioscopic image processing method of non-uniform thickness component | |
| Vanmali et al. | Multi-exposure image fusion for dynamic scenes without ghost effect |
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 |