CN110298806B - Infrared image enhancement method and system - Google Patents
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Abstract
The invention provides an infrared image enhancement method and system, wherein the method comprises the following steps: acquiring a stationary wavelet transform result and a saliency map corresponding to the infrared image; wherein the stationary wavelet transform result comprises: a low-pass sub-band and a detail sub-band; carrying out contrast increasing processing on the low-pass sub-band and carrying out linear enhancement processing on the detail sub-band to obtain an enhanced low-pass sub-band and an enhanced detail sub-band; performing low-pass filtering and threshold segmentation processing on the saliency map to obtain a processed saliency map; performing dot multiplication on the enhanced detail sub-bands and the processed saliency map to obtain fused detail sub-bands; and performing stationary wavelet inverse transformation based on the enhanced low-pass sub-band and the fused detail sub-band to obtain an enhanced infrared image. The invention can effectively improve the infrared image enhancement processing efficiency and the real-time property.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an infrared image enhancement method and system.
Background
The image enhancement technology is mainly used for improving the recognition capability of an image, selectively highlighting interesting features in the image and subtracting redundant features. Due to the characteristics of the infrared imaging technology, the infrared image generally has the problems of low contrast, blurred edge and unobvious details, so that an image enhancement technology is required to improve the quality of the infrared image.
At present, the infrared image enhancement methods suitable for the market are mainly two types, namely an image enhancement method based on a space domain and an image enhancement method based on a transform domain. The spatial domain image enhancement method is to directly process the pixels in the image, and is basically based on the gray mapping transformation of the image, and the type of the mapping transformation used depends on the purpose of enhancement. The spatial domain image enhancement method mainly comprises gray level transformation, Histogram Equalization (HE), smoothing, sharpening and the like. The transform domain enhancement method firstly converts an image in an image space into other spaces in a certain form, then performs image enhancement processing by using the special property of the space, and finally converts the image into the original image space, thereby obtaining an enhanced image. Common variations are wavelet transforms, fourier transforms, discrete cosine transforms, and the like.
However, the space complexity and the time complexity of the conventional image enhancement method are high, and the real-time requirement of the system is difficult to meet.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an infrared image enhancement method and system for improving the infrared image enhancement processing efficiency and instantaneity.
In a first aspect, the present invention provides an infrared image enhancement method, including:
s1: acquiring a stationary wavelet transform result and a saliency map corresponding to the infrared image; wherein the stationary wavelet transform result comprises: a low-pass sub-band and a detail sub-band;
s2: carrying out contrast increasing processing on the low-pass sub-band and carrying out linear enhancement processing on the detail sub-band to obtain an enhanced low-pass sub-band and an enhanced detail sub-band;
s3: performing low-pass filtering and threshold segmentation processing on the saliency map to obtain a processed saliency map;
s4: performing dot multiplication on the enhanced detail sub-bands and the processed saliency map to obtain fused detail sub-bands;
s5: and performing stationary wavelet inverse transformation based on the enhanced low-pass sub-band and the fused detail sub-band to obtain an enhanced infrared image.
Optionally, the performing, in S2, a contrast increasing process on the low-pass sub-band includes:
performing CLAHE contrast enhancement processing on the low-pass sub-band, wherein the processing algorithm is as follows:
L′＝(1-α)L+α·CLAHE(L)
wherein: l denotes a low-pass subband, L' denotes an enhanced low-pass subband, α denotes a control strength, clahe (L) denotes a limit contrast adaptive histogram equalization, and · denotes a multiplication operation.
Optionally, the processing algorithm for performing linear enhancement processing on the detail sub-bands in S2 is as follows:
v＝2.5s/t·M
wherein: s represents the coefficient magnitude in the transform domain, M represents the maximum coefficient magnitude, and t represents the first enhancement parameter; hhRepresenting horizontal direction detail sub-bands, sign (·) representing sign function operation, b representing a second enhancement parameter, v representing an intermediate parameter, tanh (·) representing hyperbolic tangent function operation, c representing a third enhancement parameter, exp (·) representing exponential function operation,
representing the enhanced horizontal subband, HvShowing the sub-bands of detail in the vertical direction,
representing the enhanced vertical detail subband, HdThe detail sub-bands in the diagonal direction are shown,
representing the enhanced diagonal detail sub-bands.
Optionally, in S4, the significance map is multiplied by each detail subband element by element to obtain a calculation formula of the fused detail subband, where the calculation formula is as follows:
wherein, H'hRepresents the horizontal detail sub-band, H ', of the final enhancement'vRepresents the vertical-direction detail sub-band, H ', of the final enhancement'dThe final enhanced diagonal detail subband is represented, S represents the saliency map, and o represents the dot product operation of the matrix.
In a second aspect, the present invention further provides an infrared image enhancement system, comprising: the device comprises a memory and a processor, wherein the memory stores executable instructions of the processor; wherein the processor is configured to perform the infrared image enhancement method of any one of the first aspects via execution of the executable instructions.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
according to the infrared image enhancement method and system, a stationary wavelet transform result and a saliency map corresponding to an infrared image are obtained; wherein the stationary wavelet transform result comprises: a low-pass sub-band and a detail sub-band; carrying out contrast increasing processing on the low-pass sub-band and carrying out linear enhancement processing on the detail sub-band to obtain an enhanced low-pass sub-band and an enhanced detail sub-band; performing low-pass filtering and threshold segmentation processing on the saliency map to obtain a processed saliency map; performing dot multiplication on the enhanced detail sub-bands and the processed saliency map to obtain fused detail sub-bands; and performing stationary wavelet inverse transformation based on the enhanced low-pass sub-band and the fused detail sub-band to obtain an enhanced infrared image. The embodiment of the invention utilizes the stationary wavelet transform of the saliency map, can carry out multi-scale and multi-direction decomposition on the image, well represents the detail information of the image, and is beneficial to the detail enhancement of the infrared image; in addition, the saliency map can be very robust to infrared images containing noise; the efficiency is higher, the real-time is good.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic diagram of an infrared image enhancement method according to an embodiment of the present invention;
FIG. 2(a) is a first infrared image;
FIG. 2(b) is a first infrared image after AMSR enhancement processing;
FIG. 2(c) is a first infrared image after BPDHE enhancement processing;
FIG. 2(d) is a first infrared image after CRM enhancement processing;
fig. 2(e) is a first infrared image after the EFF enhancement processing;
FIG. 2(f) is a first infrared image after homomorphic filter enhancement processing;
FIG. 2(g) is a first infrared image after Natural Factor enhancement processing;
FIG. 2(h) is a first infrared image after enhancement processing according to an embodiment of the present invention;
FIG. 3(a) is a second infrared image;
FIG. 3(b) is a second infrared image after AMSR enhancement processing;
FIG. 3(c) is a second infrared image after BPDHE enhancement;
FIG. 3(d) is a second infrared image after CRM enhancement processing;
fig. 3(e) is a second infrared image after the EFF enhancement process;
FIG. 3(f) is a second infrared image after homomorphic filtering enhancement processing;
FIG. 3(g) is a second infrared image after Natural Factor enhancement processing;
fig. 3(h) is a second infrared image after enhancement processing according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Fig. 1 is a schematic diagram illustrating an infrared image enhancement method according to an embodiment of the present invention, and as shown in fig. 1, first, a stationary wavelet transform and a saliency map calculation are respectively performed on an input image; secondly, respectively carrying out contrast enhancement and nonlinear enhancement on the low-pass sub-band and the detail sub-band after the stationary wavelet transform; meanwhile, low-pass filtering and threshold segmentation are carried out on the acquired saliency map; then, carrying out dot multiplication on the result after nonlinear enhancement and the result after threshold segmentation to obtain a new detail sub-band; and finally, performing inverse stationary wavelet transform on the low-pass sub-band after contrast enhancement and the new detail sub-band to obtain an enhanced infrared image.
With reference to fig. 1, the infrared image enhancement method provided in this embodiment may include:
step 1: acquiring a stationary wavelet transform result and a saliency map corresponding to the infrared image; wherein the stationary wavelet transform result comprises: a low-pass sub-band and a detail sub-band.
In this embodiment, the infrared image I is subjected to stationary wavelet transform to obtain a low-pass sub-band L and a detail sub-band Hh、HvAnd Hd(ii) a And performing significance calculation on the infrared image I to obtain a significance map S of the infrared image I.
Specifically, first, a two-dimensional fourier transform is performed on the infrared image I, and a natural logarithm is taken. The specific calculation formula is as follows:
P(f)＝φ(F(I))
L(f)＝log(A(f))
wherein F represents a two-dimensional Fourier transform,
and phi represents amplitude operation and phase operation, respectively, p (f) and l (f) represent phase spectrum and log spectrum, respectively, a (f) represents the phase of I after fourier transform, and f (I) represents the fourier transform of I.
Further, a 3 × 3 mean filter h is applied to the amplitude part of the spectrumn(f) Filtering is carried out, and the calculation formula is as follows:
V(f)＝L(f)*hn(f)
wherein: v (f) represents the result of the amplitude mean filtering of the spectrum, hn(f) Mean filter is represented, convolution operation is represented.
Further, the spectral residual r (f) is calculated as follows:
R(f)＝L(f)-V(f)
further similarly, the significance map is obtained by taking an index of the log spectrum residual error and performing inverse Fourier transform, and the calculation formula is as follows:
wherein:
showing the significance map obtained by the inverse transformation, F-1Representing an inverse fourier transform.
However, experiments have found that obtaining a saliency map of an image is not directly useful for enhancement. Because the calculated saliency map is either too bright or too dark, the dynamic range is small. Therefore, the segmented binary image can be obtained as required through corresponding threshold processing. Since the binary significance map is abrupt at the boundary. Therefore, the invention makes it smooth enough by low-pass filtering it many times, and finally enhances the smooth result.
According to the embodiment of the invention, the logarithm of the amplitude part of the image subjected to the discrete Fourier transform is taken, and the initial saliency map is calculated.
Then, the initial saliency map is compared
Threshold segmentation is performed to make it a binary image, which is then smoothed using an averaging filter to the final directly usable saliency map S.
Wherein G is3×3Mean filter, G, representing a filter kernel size of 3 x 3meanWhich is indicative of the mean value filter,
graph representing significance
The binary segmentation result represents a convolution operation.
Step 2: and carrying out contrast increasing processing on the low-pass sub-band and carrying out linear enhancement processing on the detail sub-band to obtain an enhanced low-pass sub-band and an enhanced detail sub-band.
In this embodiment, CLAHE contrast enhancement processing is performed on the low-pass sub-band, and the processing algorithm is as follows:
L′＝(1-α)L+α·CLAHE(L)
and step 3: and carrying out low-pass filtering and threshold segmentation processing on the saliency map to obtain a processed saliency map.
The steps 2 and 3 are not separated, and can be carried out simultaneously.
And 4, step 4: and performing dot multiplication on the enhanced detail sub-band and the processed saliency map to obtain a fused detail sub-band.
In this embodiment, the processing algorithm for performing linear enhancement processing on the detail subband is as follows:
v＝2.5s/t·M
and 5: and performing stationary wavelet inverse transformation based on the enhanced low-pass sub-band and the fused detail sub-band to obtain an enhanced infrared image.
In this embodiment, the enhanced detail is sub-band H'h、H'v、H'dPerforming inverse stationary wavelet transform with the enhanced low-pass sub-band L' to obtain an enhanced infrared image
In the embodiment, a stationary wavelet transform result and a saliency map corresponding to an infrared image are obtained; wherein the stationary wavelet transform result comprises: a low-pass sub-band and a detail sub-band; carrying out contrast increasing processing on the low-pass sub-band and carrying out linear enhancement processing on the detail sub-band to obtain an enhanced low-pass sub-band and an enhanced detail sub-band; performing low-pass filtering and threshold segmentation processing on the saliency map to obtain a processed saliency map; performing dot multiplication on the enhanced detail sub-bands and the processed saliency map to obtain fused detail sub-bands; and performing stationary wavelet inverse transformation based on the enhanced low-pass sub-band and the fused detail sub-band to obtain an enhanced infrared image. The embodiment utilizes the stationary wavelet transform of the saliency map, can carry out multi-scale and multi-direction decomposition on the image, well represents the detail information of the image, and is beneficial to the detail enhancement of the infrared image; in addition, the saliency map can be very robust to infrared images containing noise; the efficiency is higher, the real-time is good.
Simulation experiments prove that the method has clear details, higher contrast, better visual effect and better objective evaluation index for the infrared image enhancement problem containing noise, and the method has good real-time performance and is an effective and feasible infrared image enhancement method.
Specifically, as can be seen from the enhancement result graphs shown in fig. 2(b) to 2(h) and fig. 3(b) to 3(h), the enhanced image of the present invention not only has improved overall contrast, but also has very clear edges and surface textures of the target object.
In addition, in order to better illustrate the superiority and advancement of the present invention, the objective quality of the enhancement results obtained by the technique of the present invention and those obtained by other methods were evaluated by using the commonly used 6 typical objective evaluation indexes for image enhancement. The 6 evaluation indexes are respectively as follows: GI-F, SSEQ, ES, ACG, EME, and AVC, and in addition to SSEQ, higher values of these evaluation indexes indicate better enhanced image quality. The average value of the objective evaluation index of the two sets of experimental images is shown in table 1.
TABLE 1
As can be seen from Table 1, the 6 objective evaluation indexes obtained by the enhancement result of the invention are superior to those of other methods, so that the invention can effectively improve the definition and detail information of the image.
In conclusion, the enhanced image obtained by the stationary wavelet transform infrared image enhancement method based on the saliency map has the advantages of good visual effect, rich detail information, high contrast and high efficiency.
The invention is not limited to the examples, and any equivalent changes to the technical solution of the invention by a person skilled in the art after reading the description of the invention are covered by the claims of the invention.
The embodiment of the present invention further provides an infrared image enhancement system, including: the device comprises a memory and a processor, wherein the memory stores executable instructions of the processor; wherein the processor is configured to perform the above-described infrared image enhancement method via execution of the executable instructions.
Optionally, a memory for storing a program; a Memory, which may include a volatile Memory (RAM), such as a Random Access Memory (SRAM), a Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM), and the like; the memory may also comprise a non-volatile memory, such as a flash memory. The memory 62 is used to store computer programs (e.g., applications, functional modules, etc. that implement the above-described methods), computer instructions, etc., which may be stored in one or more memories in a partitioned manner. And the computer programs, computer instructions, data, etc. described above may be invoked by a processor.
The computer programs, computer instructions, etc. described above may be stored in one or more memories in a partitioned manner. And the computer programs, computer instructions, data, etc. described above may be invoked by a processor.
A processor for executing the computer program stored in the memory to implement the steps of the method according to the above embodiments. Reference may be made in particular to the description relating to the preceding method embodiment.
The processor and the memory may be separate structures or may be an integrated structure integrated together. When the processor and the memory are separate structures, the memory, the processor may be coupled by a bus.
In addition, embodiments of the present application further provide a computer-readable storage medium, in which computer-executable instructions are stored, and when at least one processor of the user equipment executes the computer-executable instructions, the user equipment performs the above-mentioned various possible methods.
Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. 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. Additionally, the ASIC may reside in user equipment. Of course, the processor and the storage medium may reside as discrete components in a communication device.
The present application further provides a program product comprising a computer program stored in a readable storage medium, from which the computer program can be read by at least one processor of a server, the execution of the computer program by the at least one processor causing the server to carry out the method of any of the embodiments of the invention described above.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. An infrared image enhancement method, comprising:
s1: acquiring a stationary wavelet transform result and a saliency map corresponding to the infrared image; wherein the stationary wavelet transform result comprises: a low-pass sub-band and a detail sub-band;
performing stationary wavelet transform on the infrared image I to obtain a low-pass sub-band L and a detail sub-band Hh、HvAnd Hd(ii) a Carrying out significance calculation on the infrared image I to obtain a significance map S of the infrared image I;
specifically, first, two-dimensional fourier transform is performed on the infrared image I, and a natural logarithm is taken, and a specific calculation formula is as follows:
P(f)＝φ(F(I))
L(f)＝log(A(f))
wherein F represents a two-dimensional Fourier transform,
phi represents amplitude operation and phase operation respectively, P (f) and L (f) represent phase spectrum and logarithm spectrum respectively, A (f) represents amplitude after Fourier transformation to I, F (I) represents Fourier transformation to I;
using a 3 × 3 mean filter h for the log spectrum L (f)n(f) Filtering is carried out, and the calculation formula is as follows:
V(f)＝L(f)*hn(f)
wherein: v (f) represents the result of the amplitude mean filtering of the spectrum, hn(f) Representing an averaging filter, representing a convolution operation;
calculating a spectrum residual R (f) according to the following formula:
R(f)＝L(f)-V(f)
logarithm is taken on the amplitude part of the image after discrete Fourier transform, and an initial saliency map is calculated:
then, the initial saliency map is compared
Performing threshold segmentation to make the image into a binary image, and then smoothing the binary image to a finally directly usable saliency map S by using an average filter:
wherein G is3×3Mean filter, G, representing a filter kernel size of 3 x 3meanWhich is indicative of the mean value filter,
graph representing significance
Binary segmentation results, representing convolution operations;
s2: carrying out contrast increasing processing on the low-pass sub-band and carrying out linear enhancement processing on the detail sub-band to obtain an enhanced low-pass sub-band and an enhanced detail sub-band;
s3: performing low-pass filtering and threshold segmentation processing on the saliency map to obtain a processed saliency map;
s4: performing dot multiplication on the enhanced detail sub-bands and the processed saliency map to obtain fused detail sub-bands;
s5: and performing stationary wavelet inverse transformation based on the enhanced low-pass sub-band and the fused detail sub-band to obtain an enhanced infrared image.
2. The infrared image enhancement method according to claim 1, wherein the performing contrast increasing processing on the low-pass sub-band in S2 includes:
performing CLAHE contrast enhancement processing on the low-pass sub-band, wherein the processing algorithm is as follows:
L′＝(1-α)L+α·CLAHE(L)
wherein: l denotes a low-pass subband, L' denotes an enhanced low-pass subband, α denotes a control strength, clahe (L) denotes a limit contrast adaptive histogram equalization, and · denotes a multiplication operation.
3. The infrared image enhancement method according to claim 1, wherein the processing algorithm for performing linear enhancement processing on the detail sub-bands in S2 is as follows:
v＝2.5s/t·M
wherein: s represents the coefficient magnitude in the transform domain, M represents the maximum coefficient magnitude, and t represents the first enhancement parameter; hhRepresenting horizontal direction detail sub-bands, sign (·) representing sign function operation, b representing a second enhancement parameter, v representing an intermediate parameter, tanh (·) representing hyperbolic tangent function operation, c representing a third enhancement parameter, exp (·) representing exponential function operation,
representing the enhanced horizontal subband, HvShowing the sub-bands of detail in the vertical direction,
representing the enhanced vertical detail subband, HdThe detail sub-bands in the diagonal direction are shown,
representing the enhanced diagonal detail sub-bands.
4. An infrared image enhancement system, comprising: the device comprises a memory and a processor, wherein the memory stores executable instructions of the processor; wherein the processor is configured to perform the infrared image enhancement method of any of claims 1-3 via execution of the executable instructions.
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