Time-domain-based rain line decomposition and space structure guided video rain removal method

A technology of spatial structure and time domain, applied in the field of computer vision, can solve the problems of poor rain removal effect and incomplete preservation of surrounding background details, etc., and achieve superior performance

Active Publication Date: 2020-08-18
DALIAN UNIV OF TECH
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Problems solved by technology

However, due to inaccurate rain positioning, the rain removal effect is no

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  • Time-domain-based rain line decomposition and space structure guided video rain removal method
  • Time-domain-based rain line decomposition and space structure guided video rain removal method
  • Time-domain-based rain line decomposition and space structure guided video rain removal method

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Embodiment Construction

[0032] The present invention uses a novel, end-to-end joint deep convolutional neural network to achieve video rain removal. The specific implementation steps of the method are: preparing data processing data sets-training network algorithms-testing algorithm performance results-repeated parameter adjustment make the performance optimal. The specific details are as follows: We train the single-frame enhancement module and the multi-frame fusion module separately, and then jointly train them. During network training, resize images to be larger than 352*288. Since the network is fully convolutional, and the number of training batches is set to 1, the size of the input image does not need to be determined. During the training process, the Adam optimizer (weight decay rate 0.0001) is used to iteratively update the network parameters (tuning parameters). The epoch of the light rain database is set to 80, the epoch of the heavy rain database is set to 30, and the initial learning r...

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Abstract

The invention belongs to the technical field of computer vision, and provides a time-domain-based rain line decomposition and space structure guided video rain removal method, being characterized in that rain stripes of each frame are processed from two aspects of position and intensity, and a learnable decomposition mode is defined to learn rain distribution, and a position guide graph acts on asingle-frame rain removing block related to intensity to accurately remove the rain stripes; and secondly, a multi-frame fusion module with an edge guide graph is constructed to fuse time-space information, recover a background and retain more details at the same time. A large number of experiments show that compared with other latest methods, the algorithm is excellent in video rain removal task.Ablation experiments about a network architecture fully show that our network is valid.

Description

technical field [0001] The invention belongs to the field of computer vision and relates to a video rain removal method based on rain line decomposition in time domain and spatial structure guidance. Background technique [0002] In daily life, people are often affected by severe weather, such as heavy fog, heavy snow, and heavy rain. These severe weather not only affect people's normal travel, but also cause interference and degradation of captured images or videos. [0003] In outdoor computer vision applications, rain is a relatively common severe weather condition, and it is also a challenging problem. It will cause serious intensity and light fluctuations in a small area, and the visibility of people's sight and the clarity of camera equipment will be affected. The photos and videos captured by these devices will suffer from image degradation, increased noise blocking important background information, and less application information. Such problems will be encountered i...

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Application Information

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IPC IPC(8): G06T5/00G06K9/62G06N3/04G06N3/08
CPCG06T5/003G06N3/08G06T2207/10016G06N3/045G06F18/251
Inventor 薛昕惟丁莹刘日升王祎樊鑫
Owner DALIAN UNIV OF TECH
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