Underwater image restoration method based on convolutional neural network

A convolutional neural network and underwater image technology, applied in the field of underwater image restoration based on convolutional neural network, can solve the problems of texture and detail loss, low contrast and clarity, and achieve high robustness

Active Publication Date: 2020-02-25
TIANJIN UNIV
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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...

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  • Underwater image restoration method based on convolutional neural network
  • Underwater image restoration method based on convolutional neural network
  • Underwater image restoration method based on convolutional neural network

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Embodiment Construction

[0041] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0042] Such as figure 1 As shown, the present invention provides a kind of underwater image restoration method based on convolutional neural network, and it comprises the following steps:

[0043] Step 1, Synthesize the underwater image

[0044] The quality of the training data largely determines the performance of the network. The training of the convolutional neural network requires paired underwater images and the corresponding model parameters (background light and transmittance). Since the paired data obtained through experiments The collection is very difficult and has errors, so in this embodiment, the underwater imaging model and the existing indoor depth data set are used t...

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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...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T7/50G06T7/90
CPCG06T5/001G06T7/90G06T7/50G06T2207/10024Y02A90/30
Inventor 郭继昌茹丽郭春乐
Owner TIANJIN UNIV
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