Single-image-oriented rain removal method based on cascaded hole convolutional neural network

A technology of convolutional neural network and single image, which is applied in the direction of biological neural network model, neural architecture, image enhancement, etc., to achieve the effect of generalization and integrity assurance

Active Publication Date: 2019-11-26
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0004] The purpose of the present invention is to provide a single image-oriented rain removal method based on cascaded

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  • Single-image-oriented rain removal method based on cascaded hole convolutional neural network
  • Single-image-oriented rain removal method based on cascaded hole convolutional neural network
  • Single-image-oriented rain removal method based on cascaded hole convolutional neural network

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Embodiment

[0035] This embodiment provides a method for removing rain from a single image based on a cascaded hole convolutional neural network, including the following steps:

[0036] Step 1: Construct a deraining model based on cascaded dilated convolutional neural network:

[0037] Step 1-1: Modeling the rainwater part of the rainwater image according to the model features of the rainwater image;

[0038] The image rain model widely used in academia and industry is: O=B+R. In this rain model, O is an image containing rain, B is a background layer, and R is a rain layer; but there are some shortcomings in this rain model, which will When the rain-free image is synthesized with the rain image, the overlay method of the background layer plus the rain layer is directly used. When using this model to separate the background of the rain image synthesized by the rain model, it is necessary to use the rain removal method of the model to extract the rain image. The features are identified at ...

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Abstract

The invention belongs to the technical field of image rain removal, and provides a single-image-oriented rain removal method based on a cascaded hole convolutional neural network, which is used for solving the problem of restoration of a single image shot in rainy days. The method comprises the following steps: firstly, modeling rainwater, and dividing a rain image into a rainwater region layer, arainwater layer and a background layer; extracting a rainwater region layer image from an input image through cascaded multi-channel convolutional neural networks with different void ratios, obtaining a rainwater layer image through convolution, and obtaining a rain-removed background layer image through convolution and summation of the input image. Details of different scales of the image are effectively extracted through the cascaded hole convolutional neural network, the network adopts a residual network structure to increase the network depth, and the over-fitting problem is avoided; an evaluation experiment is carried out on a public data set, and the experiment shows that compared with a single-image rain removal classic method, the peak signal-to-noise ratio (PSNR) can be improvedby 2-8, and the image structural similarity (SSIM) can be improved by 0.04-0.22.

Description

technical field [0001] The invention belongs to the technical field of image deraining, and relates to the application of deep learning in image deraining, in particular to a method for deraining a single image based on a cascaded hollow convolutional neural network. Background technique [0002] Most outdoor vision systems, such as surveillance and autonomous navigation, require accurate feature detection on outdoor scene images for the next step of system processing. In severe weather, usually such as heavy rain, the content and color of the image often change drastically and have a large difference from the original image; this rainy image will cause the loss of global image contrast and color effects, resulting in many images Details are lost. For a computer vision system that relies heavily on the quality of the input image, the result may be disastrous. For example, for the vision system of autonomous driving, the input image in rainy days is likely to cause the targe...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06N3/04
CPCG06T5/005G06N3/045
Inventor 张萍彭一凡卢韶强申奉璨蒲恬
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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