Low-illumination image processing method and device
First Claim
1. A low-illumination image processing method, comprising:
- inputting a low-illumination image;
dividing the low-illumination image into different texture regions, calculating a local standard deviation and a gradient sum of the gray scale of pixel points in various texture regions, and calculating a ratio of the local standard deviation and the gradient sum as a texture and noise level parameter of the image;
performing inverse color processing on the low-illumination image to obtain an inverse color image;
determining a first filtering coefficient and a second filtering coefficient according to the mean of the standard deviations of the gray scale of pixel points in various texture regions, and performing smoothing processing on an inverse color image by adopting the first filtering coefficient and the second filtering coefficient to obtain a first smoothing image and a second smoothing image respectively;
performing weighted averaging on the first smoothing image and the second smoothing image according to the texture and noise level parameter to obtain a weighted image;
calculating a dark channel image of the weighted image, and estimating an environment illumination intensity according to the dark channel image;
calculating a contrast enhancement coefficient according to the dark channel image and environment illumination intensity;
calculating a gradient image of the inverse color image, and extracting texture structure from the gradient image of the inverse color image to obtain a texture image;
combining the texture image with the weighted image to obtain a sharpened image; and
performing contrast enhancement on the sharpened image according to the environment illumination intensity and the contrast enhancement coefficient to obtain an enhanced image; and
performing inverse color processing on the enhanced image to obtain an output image.
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Abstract
A low-illumination image processing method and device address the problem of noise amplification in existing contrast enhancement techniques when applied to original low-illumination image. A noise suppression filter is additionally arranged before an operation of contrast enhancement, and smoothing processing is performed on an inverse color image of a low-illumination image by adopting a first filtering coefficient and a second filtering coefficient, so that image contrast is enhanced while random noise is suppressed. Texture and noise level parameter of an image are calculated according to a local characteristic inside block of the image. Weighted averaging is performed on a first smoothing image and a second smoothing image after smoothing processing according to the texture and noise level parameters. The texture image is obtained by performing texture structure extraction on a gradient image of an inverse color image, and the texture image is combined with a weighted image to sharpen the weighted image, to have an effect of enhancing image details. Therefore, the contrast of low-illumination image can be effectively enhanced, various types of noise can be filtered, and the image color and details can be retained at the same time to obtain a clear and vivid restored image.
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Citations
13 Claims
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1. A low-illumination image processing method, comprising:
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inputting a low-illumination image; dividing the low-illumination image into different texture regions, calculating a local standard deviation and a gradient sum of the gray scale of pixel points in various texture regions, and calculating a ratio of the local standard deviation and the gradient sum as a texture and noise level parameter of the image; performing inverse color processing on the low-illumination image to obtain an inverse color image; determining a first filtering coefficient and a second filtering coefficient according to the mean of the standard deviations of the gray scale of pixel points in various texture regions, and performing smoothing processing on an inverse color image by adopting the first filtering coefficient and the second filtering coefficient to obtain a first smoothing image and a second smoothing image respectively; performing weighted averaging on the first smoothing image and the second smoothing image according to the texture and noise level parameter to obtain a weighted image; calculating a dark channel image of the weighted image, and estimating an environment illumination intensity according to the dark channel image; calculating a contrast enhancement coefficient according to the dark channel image and environment illumination intensity; calculating a gradient image of the inverse color image, and extracting texture structure from the gradient image of the inverse color image to obtain a texture image; combining the texture image with the weighted image to obtain a sharpened image; and performing contrast enhancement on the sharpened image according to the environment illumination intensity and the contrast enhancement coefficient to obtain an enhanced image; and performing inverse color processing on the enhanced image to obtain an output image. - View Dependent Claims (2, 3, 4)
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5. A low-illumination image processing device, comprising:
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an input module configured to input a low-illumination image; an image segmentation module configured to divide the low-illumination image into different texture regions; a first calculation module configured to calculate a local standard deviation and a gradient sum of the gray scale of pixel points in various texture regions, and to calculate a ratio of the standard deviation and the gradient sum as a texture and noise level parameter of the image; a first inverse color module configured to perform inverse color processing on the low-illumination image to obtain an inverse color image; a smoothing filtering module configured to determine a first filtering coefficient and a second filtering coefficient according to a mean of the standard deviations of the gray scale of pixel points in various texture regions, and to perform smoothing processing on an inverse color image by adopting a first filtering coefficient and a second filtering coefficient to obtain a first smoothing image and a second smoothing image respectively; a weighting module configured to perform weighted averaging on the first smoothing image and the second smoothing image according to the texture and noise level parameter to obtain a weighted image; a second calculation module configured to calculate a dark channel image on the weighted image, and to estimate an environment illumination intensity according to the dark channel image; and
to calculate a contrast enhancement coefficient according to the dark channel image and environment illumination intensity;a third calculation module configured to calculate a gradient image on the inverse color image, and performing texture structure extraction on the gradient image of the inverse color image to obtain a texture image; a sharpening module configured to combine the texture image with the weighted image to obtain a sharpened image; a contrast enhancement module configured to perform contrast enhancement on the sharpened image according to the environment illumination intensity and the contrast enhancement coefficient to obtain an enhanced image; and a second inverse color module configured to perform inverse color processing on the enhanced image to obtain an output image. - View Dependent Claims (6, 7, 8, 9, 10, 11, 12, 13)
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Specification