Super-Resolution Enhancement of Gymnastic Action Backlighting Image Based on Improved Residual Network
Super-resolution images are beneficial to information acquisition and transmission,but there is a lack of research on backlighting low-resolution images.In order to solve the problem of shadow noise in backlighting ima-ges,this paper proposes a backlighting image super-resolution enhancement model based on an improved Retinex backlighting image decomposition and enhancement algorithm,which is called NRE-DRAN model by combining the DRAN double residual attention network.Firstly,the model decomposes the backlighting image into an illumination map and a reflection map based on Retinex theory,and processes the two-component maps by adjusting the network and restoring the network,and then fuses the processed component maps to reduce the influence of backlighting noise.Then,a DRAN feature extraction module is used to extract the shallow texture features and deep semantic features,and an information distillation module is used to enhance the feature information and concatenate them into a fused feature map.Finally,a super-resolution image is reconstructed based on the residual map and the up-sampled map.The simulation results of the multi-group baseline algorithm superposition model show that the NRE-DRAN model has the best SSIM index(an average increase of 3.93%compared with other superposition models)and the better PSNR index(the index ranks second)on the GBI gymnastics action backlighting image data set.To sum up,the NRE-DRAN backlighting image super-resolution enhancement model effectively enhances the super-resolution of the im-age while solving the problem of backlighting shadow noise,and the model has a high timeline.
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