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Image defogging method based on Bayesian convolutional neural network and storage medium

A convolutional neural network and Bayesian technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as insufficient data, complex design of deep learning models, and unreasonable weights

Pending Publication Date: 2021-04-09
SHANGHAI MARITIME UNIVERSITY
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AI Technical Summary

Problems solved by technology

However, on the one hand, the design of deep learning models is becoming more and more complex, and the amount of data is also insufficient, which can easily lead to overfitting problems
On the other hand, the existing convolutional neural network dehazing methods all use specific values ​​as weights. From the perspective of probability theory, it is unreasonable to use point estimates as weights.

Method used

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  • Image defogging method based on Bayesian convolutional neural network and storage medium
  • Image defogging method based on Bayesian convolutional neural network and storage medium
  • Image defogging method based on Bayesian convolutional neural network and storage medium

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Embodiment Construction

[0051] A Bayesian convolutional neural network-based image defogging method and storage medium proposed by the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description. It should be noted that the drawings are in a very simplified form and all use imprecise scales, which are only used to facilitate and clearly assist the purpose of illustrating the embodiments of the present invention. In order to make the objects, features and advantages of the present invention more comprehensible, please refer to the accompanying drawings. It should be noted that the structures, proportions, sizes, etc. shown in the drawings attached to this specification are only used to match the content disclosed in the specification, for those who are familiar with this technology to understand and read, and are not used to lim...

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Abstract

The invention discloses an image defogging method based on a Bayesian convolutional neural network and a storage medium, and the method comprises the steps of obtaining an RESIDE data set, employing an ITS data set in the RESIDE data set as a training set, and employing an SOTS data set as a test set; and inputting the training set into a Bayesian convolutional neural network, and training the Bayesian convolutional neural network to obtain an optimal model; and inputting the test set into the optimal model, performing end-to-end defogging processing on the test set by adopting the optimal model, and outputting a defogged image. According to the invention, the over-fitting problem can be effectively avoided, and the robustness of the defogging model is enhanced.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image defogging method and a storage medium based on a Bayesian convolutional neural network. Background technique [0002] Smog is common in cities and is a disastrous natural weather phenomenon. The formation of smog is mainly the result of a large number of particles suspended in the air and meteorological conditions. The generation of smog makes the captured outdoor images unclear and poor in contrast. The degradation of image quality makes it difficult for humans to identify the content of the image. It also affects many photography fields and computer vision tasks, such as video surveillance, object recognition, and image classification. , target tracking, etc. Therefore, how to improve low-quality images and reduce the adverse effects caused by haze is particularly important. [0003] Currently, there are two mainstream methods for image dehazing. One is a defogging m...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06N3/08G06T2207/20081G06T2207/20084G06N3/047G06N3/045G06T5/73
Inventor 严家佳李朝锋
Owner SHANGHAI MARITIME UNIVERSITY
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