An image and video enhancement method based on multi-branch convolutional neural network

Abstract

The invention provides an image and video enhancement method based on a multi-branch convolutional neural network, including: inputting a low-quality single image or video sequence, and stably solving the enhanced image or video; a novel multi-branch convolutional neural network The network structure can effectively solve the problem of image or video quality degradation caused by insufficient lighting, noise and other factors; a novel training loss function can effectively improve the accuracy and stability of the neural network. One of the applications of the present invention is unmanned vehicle (machine) driving. Its principle is to process and enhance the image quality degradation caused by changes in the surrounding environment or interference of the video sensor, thereby providing higher quality images and videos for the decision-making system. Information, which helps the decision-making system to make more accurate and correct decisions. The present invention can also be widely used in fields such as video calling, automatic navigation, video monitoring, short video entertainment, social media, and image restoration.

Description

technical field [0001] The invention relates to the fields of computer vision and image processing, in particular to an image and video enhancement method based on a multi-branch convolutional neural network. Background technique [0002] As a fundamental problem in the field of image processing, image enhancement is of great significance to many computer vision algorithms that rely on high-quality images and videos. Most of the existing computer vision algorithms are for processing high-quality pictures or videos, but in practical applications, it is difficult to obtain high-quality images and videos due to changes in cost and natural conditions. In this case, the image enhancement algorithm can be used as the preprocessing process of the computer vision algorithm to improve the quality of the input image and video of the computer vision algorithm, thereby improving the accuracy of the computer vision algorithm and generating practical application value. [0003] In recent...

AI Technical Summary

Problems solved by technology

[0007] Among the five types of methods, the first four methods belong to the traditional enhancement methods, and the effect is far behind the deep learning methods that have emerged in recent years. However, most of the existing deep learning methods are researched on a special scenario, such as Noise, smog, low light, etc.

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