Multi-exposure image fusion method based on attention generative adversarial network

Abstract

The invention provides a multi-exposure image fusion method based on an attention generative adversarial network. The thought of the attention mechanism is highly matched with the detail weighting problem in multi-exposure fusion, the weight of each input image can be adaptively selected by applying channel attention, and the weights of different spatial positions are adaptively selected by usingspatial attention. The technology has a wide application prospect in various multimedia vision fields. According to the algorithm, a new attention generative adversarial network is designed to be usedfor a multi-exposure image fusion task, and a visual attention mechanism is introduced into the generative network, so that the network can be helped to adaptively learn weights of different input images and different spatial positions to achieve a better fusion effect.

Description

technical field [0001] The invention belongs to the field of digital image / video signal processing, in particular to a multi-exposure image fusion method based on attention generation confrontation network. Background technique [0002] With the development of computer and multimedia technology, various multimedia applications have put forward extensive demands on high-quality images. High-quality images can provide rich information and real visual experience. However, during the image acquisition process, due to the influence of image acquisition equipment, acquisition environment, noise and other factors, the images presented on the display terminal are often low-quality images. Therefore, how to reconstruct high-quality images from low-quality images has always been a challenge in the field of image processing. [0003] From bright sunlight to dim starlight, illumination intensities in natural scenes can span a very large dynamic range, with brightness contrasts exceedi...

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to overcome the fact that the existing multi-exposure image fusion method relies on artificially defined fusion weights in different calculation methods, aiming at the problem of image dynamic range enhancement based on the multi-exposure image fusion method, it provides a multi-exposure based on attention generation confrontation network Fusion method, the generative confrontation network can use the channel attention mechanism to adaptively determine the weight of each input, use the spatial attention mechanism to adaptively determine the weight of any spatial position, and realize data-driven adaptive end-to-end multi-exposure images fusion

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