CN108961172B - Gamma correction-based image contrast enhancement method - Google Patents
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- 239000000126 substance Substances 0.000 claims description 5
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- 230000003044 adaptive effect Effects 0.000 claims description 3
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- 230000008569 process Effects 0.000 claims description 2
- 230000002708 enhancing effect Effects 0.000 claims 2
- 230000000007 visual effect Effects 0.000 abstract description 6
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
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Abstract
The invention discloses an image contrast enhancement method based on Gamma correction, and relates to the technical field of digital image processing. The method obtains a low-frequency image I by performing wavelet decomposition on an input imageAAnd three high frequency images IH、IVAnd ID(ii) a And for low frequency image IACarrying out self-adaptive Gamma correction to obtain a low-frequency image with enhanced contrast; for high-frequency images I simultaneouslyH、IVAnd IDCarrying out denoising treatment to obtain a denoised high-frequency image; and finally, performing wavelet inverse transformation on the low-frequency image with enhanced contrast and the denoised high-frequency image to obtain a reconstructed image, wherein the result shows that the method effectively improves the subjective visual effect of the image, effectively inhibits the noise of the image, improves the brightness distribution of the image and improves the overall contrast of the image.
Description
Technical Field
The invention relates to the technical field of digital image processing, in particular to an image contrast enhancement method based on Gamma correction.
Background
Image enhancement is an important pre-processing step in image processing. The method can effectively improve the quality of the image, improve the subjective visual effect of the image and highlight the useful characteristics of the image. Gamma correction is one of the most widely used image enhancement algorithms. Gamma correction adopts power function to correct the image gray level, thus achieving the purpose of improving the image brightness distribution. However, when the power exponent is small, the contrast of the Gamma-corrected image tends to be low. To solve this problem, researchers have proposed a number of correction algorithms based on Gamma correction. However, these algorithms often face problems such as image noise amplification, insignificant contrast improvement, or excessive calculation.
Disclosure of Invention
The present invention is directed to an image contrast enhancement method based on Gamma correction, so as to solve the foregoing problems in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
an image contrast enhancement method based on Gamma correction comprises the following steps:
s1, carrying out wavelet decomposition on the input image to obtain a low-frequency image IAAnd three high frequency images IH、IVAnd ID；
S2, for low-frequency image IACarrying out self-adaptive Gamma correction to obtain a low-frequency image with enhanced contrast;
s3, respectively aligning the high-frequency images IH、IVAnd IDCarrying out denoising treatment to obtain a denoised high-frequency image;
and S4, performing wavelet inverse transformation on the contrast-enhanced low-frequency image obtained in the S2 and the de-noised high-frequency image obtained in the S3 to obtain a reconstructed image.
Preferably, S2 includes the steps of: s1 concretely, performing one-layer wavelet decomposition on the input image by using a Haar wavelet basis to obtain a low-frequency image IAAnd three high-frequency images IH、IVAnd ID。
Preferably, S2 includes the steps of:
s201, aiming at the low-frequency image IABilateral filtering is carried out to obtain a filtered low-frequency image FA；
S202, based on the filtered low-frequency image FAFor the low-frequency image IAThe adaptive Gamma correction is performed according to the following formula:
wherein the content of the first and second substances,
IMAXfor low-frequency images IAThe maximum value of the gray scale is,
m x N is the original image size,
gamma is a correction parameter.
Preferably, the correction parameter γ is based on the low-frequency image IAIs selected, the smaller the grey value is, the larger the correction factor gamma is.
Preferably, in the bilateral filtering, the window size, the distance variance, and the gray variance are set to [5,10], [30,50], and [30,50], respectively, and the correction parameter is [0.5,0.9 ].
Preferably, S3 is embodied as follows, based on the relative detail image R, respectively for the high frequency image IH、IVAnd IDCarrying out threshold denoising:
wherein the content of the first and second substances,
t is the threshold value of the noise removal,
Norm(Iinput) Representing the image IinputThe grey value normalization operation is carried out and,
max(Iinput) And min (I)input) Respectively represent IinputA gray maximum value and a gray minimum value.
Preferably, the denoising threshold T is selected empirically according to the noise level of the image to be processed.
Preferably, the denoising threshold T is selected to be [0.02,0.1 ].
The invention has the beneficial effects that: according to the image contrast enhancement method based on Gamma correction provided by the embodiment of the invention, the low-frequency image I is obtained by performing wavelet decomposition on the input imageAAnd three high frequency images IH、IVAnd ID(ii) a And for low frequency image IACarrying out self-adaptive Gamma correction to obtain a low-frequency image with enhanced contrast; for high-frequency images I simultaneouslyH、IVAnd IDCarrying out denoising treatment to obtain a denoised high-frequency image; final contrast enhancement lowThe wavelet inverse transformation is carried out on the frequency image and the denoised high-frequency image to obtain a reconstructed image, and the result shows that the method effectively improves the subjective visual effect of the image, effectively inhibits the noise of the image, improves the brightness distribution of the image and improves the integral contrast of the image.
Drawings
FIG. 1 is a schematic flow chart of an image contrast enhancement method based on Gamma correction according to the present invention;
FIG. 2 is an original image before processing in an exemplary embodiment;
FIG. 3 is a relative detail image obtained during implementation of the method;
fig. 4 is an image obtained by processing an original image by applying the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, an embodiment of the present invention provides an image contrast enhancement method based on Gamma correction, including the following steps:
s1, carrying out wavelet decomposition on the input image to obtain a low-frequency image IAAnd three high frequency images IH、IVAnd ID；
S2, for low-frequency image IACarrying out self-adaptive Gamma correction to obtain a low-frequency image with enhanced contrast;
s3, respectively aligning the high-frequency images IH、IVAnd IDCarrying out denoising treatment to obtain a denoised high-frequency image;
and S4, performing wavelet inverse transformation on the contrast-enhanced low-frequency image obtained in the S2 and the de-noised high-frequency image obtained in the S3 to obtain a reconstructed image.
Wherein, S1 may specifically be, performing one-layer wavelet decomposition on the input image by using Haar wavelet basis to obtain a low-frequency image IAAnd three panelsHigh frequency image IH、IVAnd ID。
S2 may include the steps of:
s201, aiming at the low-frequency image IABilateral filtering is carried out to obtain a filtered low-frequency image FA；
S202, based on the filtered low-frequency image FAFor the low-frequency image IAThe adaptive Gamma correction is performed according to the following formula:
wherein the content of the first and second substances,
IMAXfor low-frequency images IAThe maximum value of the gray scale is,
m x N is the original image size,
gamma is a correction parameter.
Wherein, in the correction process, the correction parameter gamma can be based on the low-frequency image IAIs selected, the smaller the grey value is, the larger the correction factor gamma is.
In the embodiment of the present invention, in the bilateral filtering, the window size, the distance variance, and the gray variance may be set as [5,10], [30,50], and [30,50], respectively, and the correction parameter is [0.5,0.9 ].
Experimental results show that the subjective visual effect of the image can be effectively improved by adopting the parameter combination.
In a preferred embodiment of the present invention, S3 may be specifically that the high-frequency images I are respectively processed based on the relative detail image R according to the following formulaH、IVAnd IDCarrying out threshold denoising:
wherein the content of the first and second substances,
t is the threshold value of the noise removal,
Norm(Iinput) Representing the image IinputPerforming a grey value normalization operation IinputComprises IA、IH、IVAnd ID，max(Iinput) And min (I)input) Respectively represent IinputA gray maximum value and a gray minimum value.
In particular, the method comprises the following steps of,
in the method, the high-frequency image contains detail information and noise of the image, and the noise of the image can be effectively removed by carrying out threshold denoising on the high-frequency image based on the relative detail image R.
According to the method provided by the embodiment of the invention, the denoising threshold T can be selected based on experience according to the noise level of the image to be processed.
The larger the denoising threshold value is, the more obvious the denoising effect is, and the less detail is retained in the image.
In a preferred embodiment of the present invention, the denoising threshold T may be selected to be [0.02,0.1 ].
Experimental results show that the noise level of the image can be effectively reduced for most of the images to be processed by selecting the denoising threshold value in the range, and meanwhile, the details of the image are not lost.
In the embodiment of the present invention, wavelet inverse transformation is performed on the contrast-enhanced low-frequency image obtained in S2 and the denoised high-frequency image obtained in S3 by using the prior art, so as to obtain a reconstructed image.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
In the embodiment of the present invention, the contrast enhancement method for an image based on Gamma correction provided by the present invention is adopted to perform contrast enhancement on the original image shown in fig. 2, wherein the size of the bilateral filtering window, the distance variance and the gray variance are respectively set to 5, 30 and 30; the denoising threshold T is 0.02, the correction coefficient γ is 0.8, the obtained relative detail image R is shown in fig. 3, and the finally obtained processed enhanced image is shown in fig. 4. By comparing fig. 4 and fig. 2, it can be seen that the method provided by the present invention can effectively improve the subjective visual effect of the image: the noise of the image is effectively suppressed; the brightness distribution of the image is improved; the overall contrast of the image is improved. Therefore, the method provided by the invention has the advantages of effectiveness, reasonability, feasibility and scientificity.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained: according to the image contrast enhancement method based on Gamma correction provided by the embodiment of the invention, the low-frequency image I is obtained by performing wavelet decomposition on the input imageAAnd three high frequency images IH、IVAnd ID(ii) a And for low frequency image IACarrying out self-adaptive Gamma correction to obtain a low-frequency image with enhanced contrast; for high-frequency images I simultaneouslyH、IVAnd IDCarrying out denoising treatment to obtain a denoised high-frequency image; and finally, performing wavelet inverse transformation on the low-frequency image with enhanced contrast and the denoised high-frequency image to obtain a reconstructed image, wherein the result shows that the method effectively improves the subjective visual effect of the image, effectively inhibits the noise of the image, improves the brightness distribution of the image and improves the overall contrast of the image.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.
Claims (7)
1. An image contrast enhancement method based on Gamma correction is characterized by comprising the following steps:
s1, carrying out wavelet decomposition on the input image to obtain a low-frequency image IAAnd three high frequency images IH、IVAnd ID；
S2, for low-frequency image IACarrying out self-adaptive Gamma correction to obtain a low-frequency image with enhanced contrast;
s3, respectively aligning the high-frequency images IH、IVAnd IDCarrying out denoising treatment to obtain a denoised high-frequency image; s3 is to process the high frequency image I based on the relative detail image R according to the following formulaH、IVAnd IDCarrying out threshold denoising:
where M × N is the original image size, i 1, 2., M/2, j 1, 2., N/2,
t is the threshold value of the noise removal,
Norm(Iinput) Representing the image IinputThe grey value normalization operation is carried out and,
max(Iinput) And min (I)input) Respectively represent IinputA gray maximum value and a gray minimum value of;
and S4, performing wavelet inverse transformation on the contrast-enhanced low-frequency image obtained in the S2 and the de-noised high-frequency image obtained in the S3 to obtain a reconstructed image.
2. The method for enhancing image contrast based on Gamma correction according to claim 1, wherein S2 comprises the following steps: s1 concretely, performing one-layer wavelet decomposition on the input image by using a Haar wavelet basis to obtain a low-frequency image IAAnd three high-frequency images IH、IVAnd ID。
3. The method for enhancing image contrast based on Gamma correction according to claim 1, wherein S2 comprises the following steps:
s201, aiming at the low-frequency image IABilateral filtering is carried out to obtain a filtered low-frequency image FA；
S202, based on the filtered low-frequency image FAFor the low-frequency image IAThe adaptive Gamma correction is performed according to the following formula:
wherein the content of the first and second substances,
IMAXfor low-frequency images IAThe maximum value of the gray scale is,
m x N is the original image size,
gamma is a correction parameter.
4. The method of claim 3, wherein the Gamma correction-based image contrast enhancement method is performed according to the low-frequency image IAIs selected, the smaller the grey value is, the larger the correction factor gamma is.
5. The method of claim 4, wherein in the bilateral filtering, the window size, the distance variance and the gray variance are set to [5,10], [30,50] and [30,50], respectively, and the correction parameter is [0.5,0.9 ].
6. The Gamma correction-based image contrast enhancement method as claimed in claim 1, wherein the denoising threshold T is selected empirically based on the noise level of the image to be processed.
7. The Gamma correction based image contrast enhancement method according to claim 6, wherein the denoising threshold T is selected to be [0.02,0.1 ].
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| CN110136084A (en) * | 2019-05-16 | 2019-08-16 | 哈尔滨工业大学（深圳） | Automatic CLAHE image enchancing method, device, system and storage medium based on wavelet transform |
| CN110288546B (en) * | 2019-06-27 | 2022-11-01 | 华侨大学 | Low-illumination image enhancement method adopting bidirectional gamma conversion |
| CN112862711B (en) * | 2021-01-29 | 2022-09-16 | 山东建筑大学 | Method and device for correcting gray value of image |
| CN113256532A (en) * | 2021-06-13 | 2021-08-13 | 石家庄职业技术学院 | Low-contrast character image enhancement method |
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