A joint learning deep network model for optical flow estimation and denoising of video images

Abstract

The invention discloses a joint learning deep network model for optical flow estimation and denoising of video images, which belongs to the field of image processing. The model includes a preprocessing module, an optical flow estimation module, and a denoising module. Each module adopts the Encoder-Decoder network structure. Using the sample data set, the preprocessing module is first trained separately, and then the relevant parameters of the preprocessing module are fixed, and the preprocessing module is trained at the same time. module and the optical flow estimation module, and finally fix the relevant parameters of the preprocessing module and the optical flow estimation module. The overall training includes a deep network model of three modules. Using the trained deep network model, the optical flow estimation and removal of noisy video images can be directly performed. noise processing. The joint learning deep network model proposed by the present invention has fast optical flow estimation and denoising speed and high precision, and is convenient for quickly processing a large number of video images in practice.

Description

technical field [0001] The invention relates to the field of image processing, and specifically refers to a joint learning deep network model for optical flow estimation and denoising of video images. Background technique [0002] Video images are faced with noise interference during acquisition, compression, storage, transmission and other links. Noise will significantly reduce the visual quality of video images and cause difficulties for subsequent intelligent analysis such as target recognition and tracking. Therefore, it is necessary to remove the noise in the video image under the premise of retaining the video information, improve the signal-to-noise ratio and improve the visual effect. [0003] Because video images have time-domain correlation, optical flow estimation and video denoising can be combined to obtain better denoising effects. However, the existing joint optical flow estimation and video denoising algorithms require a large number of iterative operations a...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the above disadvantages, the present invention aims to provide a joint learning deep network model for optical flow estimation and denoising of video images, using the deep network model to jointly learn optical flow estimation and video denoising from a large number of training samples to achieve Solve the problems of low optical flow estimation accuracy, poor denoising effect and long time consumption in the existing technology

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