A video denoising method based on prior information and convolutional neural network

Abstract

The invention belongs to the field of video processing, in particular relates to video enhancement technology, and specifically provides a video denoising method based on prior information and a convolutional neural network. The present invention denoises the noise video based on the convolutional neural network, and constructs a denoising neural network composed of two parts connected, wherein the first part is a 4-layer 1×1 convolution kernel connected in sequence, and each convolution kernel The ReLU activation function is connected; the second part is a 15-layer 3×3 Octave convolution kernel connected in sequence, and the first to 14th layer convolution kernels are connected with batch normalization and ReLU activation functions; at the same time, fully in the training set construction and waiting time During the preprocessing process of noise video data, the front and back frame information is fully utilized. To sum up, compared with the traditional method, the present invention does not need to manually adjust parameters, has a good denoising effect, can well maintain texture details in the video, is easy to use, runs fast, and has high robustness.

Description

technical field [0001] The invention belongs to the field of video processing, in particular relates to video enhancement technology, and specifically provides a video denoising method based on prior information and a convolutional neural network. Background technique [0002] In daily life, due to the limitations of shooting conditions and the influence of sending equipment, transmission equipment, and receiving equipment, video is often disturbed by noise, which degrades video quality, affects the visual effect of video, and hinders further processing of video. Therefore, in order to obtain high-quality digital images, it is necessary to denoise images and videos while maintaining the original information as much as possible. [0003] Traditional image and video denoising methods will introduce artificial noise or greatly blur the image when filtering out noise particles, so there are great limitations; while other methods have better denoising effects, but some image edge...

AI Technical Summary

Problems solved by technology

[0003] Traditional image and video denoising methods will introduce artificial noise or greatly blur the image when filtering out noise particles, so there are great limitations; while other methods have better denoising effects, but some image edge information is in the Lost during denoising, or the algorithm is inefficient due to the computational complexity

The classic image quality enhancement method is very mature, but in the face of increasing demand, its performance cannot meet various more complex problems; new and efficient image quality enhancement methods have received sufficient attention

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