An image restoration method under extreme conditions based on convolutional neural network

A convolutional neural network and extreme condition technology, applied in the field of image restoration, can solve the problems that cannot be widely promoted, and achieve the effect of fast imaging under low light

Active Publication Date: 2021-05-25
HANGZHOU DIANZI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Thanks to the rise of neural networks in recent years, many researchers have applied neural networks to the field of image denoising, and achieved good results, but most methods are only for synthetic blurred images or only for specific noise images , and cannot be widely promoted

Method used

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  • An image restoration method under extreme conditions based on convolutional neural network
  • An image restoration method under extreme conditions based on convolutional neural network
  • An image restoration method under extreme conditions based on convolutional neural network

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Embodiment Construction

[0029] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0030] figure 1 It is the overall flow chart of the present invention. The system takes .RAW data as input, and its shape is H×W×1. The input data is packaged and divided into four channels. The packaged shape is H / 2×W / 2×4. The schematic diagram of the packaging process is as follows figure 2 As shown, it is a schematic diagram of the .RAW data format captured by the Sony α7S II camera. From the figure, it can be clearly seen that the R, G, and B pixels in the image array are evenly spaced, and the numbers of R, G, and B are more than It is 1:2:1. In order to make the image shape of the input neural network the same, we divide the .RAW image array into four channels, and the spatial resolution of each channel is only half of the original image.

[0031] After dividing the .RAW image array into four channels, we need to "zero" each channel, that is, subtract "Blac...

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Abstract

The invention discloses an image restoration method under extreme conditions based on a convolutional neural network. The present invention comprises the following steps: step 1: preprocessing the acquired short-exposure image; step 2: inputting the preprocessed image into the U-Net convolutional neural network for training; step 3: calculating the error and performing iterative training; Step 4: Evaluate the training model, and use the peak signal-to-noise ratio and structural similarity of the image as the final result evaluation criteria. The invention effectively solves the problem of rapid imaging under low light, and also provides a new feasible method for image denoising and deblurring.

Description

technical field [0001] The invention relates to the field of image restoration, in particular to an image restoration method under extreme conditions based on a convolutional neural network. Background technique [0002] In a low-light environment, the video images collected by the imaging system are mixed with useful information and noise due to the low ambient illumination, resulting in unclear target features, low definition, and difficult to distinguish. Digital image processing is required. Technology to reduce the impact of noise on the recognition of the scene by the human eye. At the same time, in the urban environment at night, due to the small coverage of urban lighting, there are more blind spots in urban roads. Therefore, it poses a serious challenge to nighttime video detection. The main manifestations are: first, the image presents a large number of dark areas, the content of which is blurred and the details are lost; second, under artificial light sources, s...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06T5/002G06N3/08G06N3/045
Inventor 颜成钢王瑞海杨洪楠王兴政孙垚棋张继勇张勇东
Owner HANGZHOU DIANZI UNIV
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