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Single image rainfall removing method based on image partitioning of generating antagonistic network

A single image, image block technology, applied in biological neural network model, image enhancement, image data processing, etc.

Active Publication Date: 2019-02-19
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This effectively overcomes the problem that many image details are ignored, and can remove the rain line as much as possible

Method used

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  • Single image rainfall removing method based on image partitioning of generating antagonistic network
  • Single image rainfall removing method based on image partitioning of generating antagonistic network
  • Single image rainfall removing method based on image partitioning of generating antagonistic network

Examples

Experimental program
Comparison scheme
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Embodiment

[0075] This embodiment provides a method for removing rain from a single image based on image block generation of the confrontation network, which includes the following steps:

[0076] Step 1: Build an image database for training the entire model.

[0077] The database includes two parts: training set and test set. The training set contains rain images and their corresponding rainless images. Among them, the rain image is synthesized by artificially adding rain lines, and the shape, intensity and direction of the rain lines are diverse to ensure the generalization ability. The test set is divided into two categories: synthetic picture test set and natural picture test set. The composite picture test set contains images without rain, so at the end, quantitative indicators such as PSNR can be calculated to evaluate the image quality of images without rain after rain. The natural picture test set can be used to test whether the trained model is feasible in real life. Because the ...

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Abstract

The invention provides a single image rainfall removing method based on image partitioning of generating antagonistic network. By dividing images into image blocks with the same size and non-overlapping each other, each image block is used as a condition to generate input of antagonistic network, and the input dimension is reduced. A generate antagonism network is trained to realize the nonlinearmapping from rainless image block to rainless image block, which overcomes the problem that many details are neglected and can remove the rainline at every scale as much as possible. In order to maintain the consistency of the structure and color between the rainless image blocks, a new error function is constructed by using bilateral filter and non-mean local denoising algorithm, which is added to the total error function of the conditional generation countermeasure network. The invention does not need any prior knowledge, nor does it need to preprocess and post-process the image, thus ensuring the integrity of the whole structure. The results on the test set show that the invention is improved by 4-7dB compared with the classical algorithm.

Description

Technical field [0001] The invention relates to a method in the technical field of single image enhancement processing, in particular to a method for removing rain from a single image based on image block generation against a network. Background technique [0002] Severe weather in nature, such as rain and fog, is inevitable in real life. These conditions will bring many negative effects to the captured images, such as: causing the structure in the scene to be deformed, and the captured images are blurred. This will seriously degrade the quality of the acquired image or video, and ultimately affect the realization of image understanding tasks such as target detection, segmentation, and recognition. In the harsh conditions of rainy days, how to remove rain lines is particularly important. Because the rain line will bring different types of visual degradation, and the image to remove the rain has always been a challenge for assisted driving systems. For images taken from a long ...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06T5/73
Inventor 徐奕倪冰冰谌乔波
Owner SHANGHAI JIAO TONG UNIV
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