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, which comprises the following steps: inputting a single image or video sequence of low quality to stably solve the enhanced image or video. A novel multi-branch convolutional neural network structure can effectively solve the problem of image or video quality degradation caused by insufficient illumination, noise and other factors. A novel training loss function can effectively improve the accuracy and stability of the neural network. One application of the invention is unmanned vehicle (aircraft) driving. The principle of the invention is to process and enhance the image quality degradation caused by the surrounding environment change or interference of the video sensor, thereby providing higher quality image and video information for the decision-making system, thereby facilitating the decision-making system to make more accurate and correct decisions. The invention canalso be widely used in the fields of video communication, automatic navigation, video monitoring, short video entertainment, social media, image restoration and the like.

Description

Technical field [0001] The invention relates to the field 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. Existing computer vision algorithms mostly process high-quality pictures or videos. However, in actual applications, it is difficult to obtain high-quality images and videos due to changes in costs 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 years, deep learning...

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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