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Self-supervision low-illumination image enhancement and denoising method based on deep learning

A technology of image enhancement and deep learning, applied in image enhancement, image analysis, image data processing, etc., can solve problems such as inability to guarantee good contrast and difficulty in suppressing noise

Active Publication Date: 2021-02-02
HARBIN INST OF TECH
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  • Claims
  • Application Information

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Problems solved by technology

[0008] In order to solve the problems existing in the existing self-supervised low-illuminance image enhancement methods, such as the inability to ensure that the enhanced result has good contrast and difficulty in suppressing noise, the present invention proposes a self-supervised low-illuminance image enhancement and denoising method based on deep learning

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  • Self-supervision low-illumination image enhancement and denoising method based on deep learning
  • Self-supervision low-illumination image enhancement and denoising method based on deep learning
  • Self-supervision low-illumination image enhancement and denoising method based on deep learning

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specific Embodiment approach 1

[0059] Specific implementation mode one: combine figure 1 To describe this embodiment,

[0060] A self-supervised low-light image enhancement and denoising method based on deep learning, comprising the following steps:

[0061] Step 1. Design an image enhancement network based on deep learning, which can decompose a low-light image into a reflection image R and an illumination image I;

[0062] The low-illuminance image S′ to be enhanced and its maximum channel image S′ max And its expected maximum channel image S' expect_max The matrix merged into M*N*5 is used as the input of the self-supervised low-light image enhancement network, and the low-light image is enhanced and denoised using the trained self-supervised low-light image enhancement network, and the output is the reflection image R and the illumination image I. The reflection image R output by the self-supervised low-light image enhancement network is the enhanced image.

[0063] The low-light image S' to be en...

specific Embodiment approach 2

[0100] A self-supervised low-light image enhancement and denoising method based on deep learning described in this embodiment, the specific process of the third step includes the following steps:

[0101] Step 31. Extract the maximum value channel image S of the low-illumination image S max

[0102]

[0103] Among them, S max (i, j) is the maximum channel image S max The i-th row and the j-th column element; max represents the maximum value operation; c is r, g, b, corresponding to the three color channels of red, green and blue in the rgb color space, S c (i,j) is the i-th row and j-th column element of a certain channel of the low-illuminance image S in the rgb color space;

[0104] Step 32, use any contrast enhancement method such as Gamma transform to the maximum value channel image S max Do a contrast enhancement operation to get the desired maximum channel image S expect_max .

[0105] Other steps and parameters are the same as those in the first embodiment.

specific Embodiment approach 3

[0106] Specific implementation mode three: combination figure 2 To describe this embodiment,

[0107] A self-supervised low-light image enhancement and denoising method based on deep learning described in this embodiment, the image enhancement network is specifically as follows:

[0108] The input is input to the first convolutional layer and the second convolutional layer respectively. The first convolutional layer and the second convolutional layer are respectively connected to a 9*9 convolutional layer and connected to an LReLU layer and a 3*3 convolutional layer. A LReLU layer;

[0109] The first convolutional layer is connected to the third convolutional unit, and the third convolutional unit is connected to an LReLU layer after the convolutional layer of 3*3;

[0110] The third convolution unit is connected to the fourth convolution unit, the fourth convolution unit is connected to the fifth convolution unit, the fifth convolution unit is connected to the sixth convol...

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Abstract

The invention discloses a self-supervision low-illumination image enhancement and denoising method based on deep learning, and belongs to the field of digital image processing. The invention aims to solve the problems that an existing self-supervised low-illumination image enhancement method based on deep learning is difficult to suppress noise and cannot directly adjust and enhance the image contrast. The method comprises a self-supervised low-illumination image enhancement network and a regular term for noise suppression, the network can be combined with any existing contrast adjustment method such as Gamma transform to realize network self-supervised training, and the noise suppression regular term can be used for a loss function during network training to enable the network to have noise suppression capability. According to the invention, the contrast and brightness of the low-illumination image can be enhanced, the color and detail information can be retained, and the noise can besignificantly suppressed. The invention can be used for enhancing and denoising the low-illumination image.

Description

technical field [0001] The invention belongs to the field of digital image processing and relates to methods for enhancing and denoising low-illuminance images. Background technique [0002] Images acquired at night or in dark indoor and other low-light environments often have problems such as low contrast, low brightness, and high noise. In recent years, researchers have proposed a variety of different image enhancement methods, including traditional methods and deep learning-based methods. [0003] Traditional methods include histogram equalization, Gamma transformation, methods based on Retinex theory and improved methods based on these methods, etc. These methods often focus on improving the contrast and brightness of the image, and cannot suppress noise well, and even bring noise Issues such as magnification and color distortion. [0004] Methods based on deep learning can be divided into two types: unsupervised and supervised, and self-supervised methods are unsuperv...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06N3/04
CPCG06T2207/20081G06N3/045G06T5/70
Inventor 张雨遆晓光李青岩闫诗雨张斌杨国辉崔天祥王春晖
Owner HARBIN INST OF TECH
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