Underwater image restoration method based on convolutional neural network

Abstract

The invention discloses an underwater image restoration method based on a convolutional neural network. The method comprises the following steps: (1) establishing an underwater optical imaging model;(2) integrating the underwater imaging model and existing indoor depth data into training data; (3) establishing a parameter estimation network, wherein the parameter estimation network comprises a sharing layer, a global background light estimation sub-network and a red channel transmissivity estimation sub-network, the sharing layer extracts common features for the two sub-networks, and the global background light estimation sub-network and the red channel transmissivity sub-network take output of the sharing layer as input and respectively map the input to global background light and red channel transmission; (4) restoring the underwater image; after a predicted global background light and red channel transmissivity graph is obtained through a parameter estimation network, calculating the transmissivity of a blue-green channel according to inherent characteristics of a water body, and finally, restoring an underwater image, and obtaining a clear underwater image.

Description

technical field [0001] The invention belongs to the technical fields of image processing and computer vision, and relates to an underwater image restoration method based on a convolutional neural network. Background technique [0002] Under the circumstances of rapid population expansion, depletion of land resources and deteriorating environment, the development of marine resources is a far-reaching strategic choice facing the 21st century. Therefore, the importance of the theory and technology of marine information acquisition, transmission and processing is more prominent. The visual quality of underwater images plays an extremely important role in marine engineering applications and scientific research, such as underwater ecological research, ocean rescue, underwater oil pipeline leakage monitoring and other application scenarios. Due to the effects of special light absorption and scattering, images captured underwater often have disadvantages such as low contrast, limit...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for restoring underwater images based on convolutional neural networks. On the basis of the underwater imaging model, the present invention learns the corresponding background of the underwater image The relationship between light and transmittance, to study an underwater image restoration method that can effectively correct the color deviation of underwater images, solve the problems of low contrast and clarity, loss of texture and details, etc.

Images