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A fast image de-smog system based on depth convolution neural network

A deep convolution and neural network technology, applied in the field of image processing, can solve the problems of inconsistent expectations, gray photos, affecting the quality of pictures, etc., to achieve the effect of real-time application and good haze removal effect.

Inactive Publication Date: 2019-02-15
SICHUAN CHANGHONG ELECTRIC CO LTD
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Problems solved by technology

Smog has become the norm. No matter which city you live in, smog can only be classified as severe or severe. So the pictures taken outdoors on a smoggy day will often seriously affect the quality of the picture due to the weather, and the photos taken show a kind of gray. Misty effect, not as expected

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  • A fast image de-smog system based on depth convolution neural network
  • A fast image de-smog system based on depth convolution neural network

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[0026] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other implementations obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.

[0027] Such as Figure 1-2 As shown, a fast image haze removal system based on a deep convolutional neural network, constructs an efficient single image haze removal depth convolutional neural network model, and a haze image training set, and trains the network proposed by the present invention, Finally, a clear haze-free image is reconstructed, which specifically includes the following steps:

[0028] Step 1: Construct a high-performance, fast single image ha...

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Abstract

The invention discloses a fast image smog removing system based on a depth convolution neural network, which constructs an efficient single image smog removing depth convolution neural network model and a smog image training set, trains the network proposed by the invention, and finally reconstructs a clear smog-free image. By constructing deep convolution neural network model and atmospheric scattering model and training them in depth, the smog removal effect can be obtained quickly and the real-time application can be realized. The system of the present invention can be easily extended to other image or video processing fields, such as image or video deblurring, image or video restoration, and the like.

Description

technical field [0001] The invention relates to the technical problem of haze removal in the field of image processing, in particular to a fast image haze removal system based on a deep convolutional neural network. Background technique [0002] In recent years, air pollution has been serious and air quality has gradually declined. Smog is the result of the interaction between specific climate and human activities. The economic production and social activities of a high-density population will emit a large amount of fine particles. Once the emission exceeds the atmospheric circulation and carrying capacity, the suspended particles will continue to accumulate, and large-scale smog will easily appear. Smog has become the norm. No matter which city you live in, smog can only be classified as severe or severe. So the pictures taken outdoors on a smoggy day will often seriously affect the quality of the picture due to the weather, and the photos taken show a kind of gray. The m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T2207/20084G06T2207/20081G06T5/73
Inventor 何娟刘蛟万蕾
Owner SICHUAN CHANGHONG ELECTRIC CO LTD
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