Single Fuzzy Image Super-Resolution Reconstruction Based On Transformer Control Network

The research on joint deblurring and super-resolution of single-image has significant theoretical significance and practical application value in the field of computer vision.Image blur and low-resolution problems are prevalent in real-world shooting scenarios,which may lead to a decrease in image quality and consequently affect image recognition and analysis.Traditional image processing methods usually require separate treatment of blur and low-resolution issues,which can increase computational complexity and computation time,affecting algorithm performance.Therefore,adopting deep learning techniques to carry out research on joint deblurring and super-resolution of single-image can ensure image quality while improving processing efficiency and recognition accuracy.By studying this method,more efficient and accurate image processing algorithms can be provided for the computer vision field,thereby promoting the development of related application areas.(1)This study first conducted an in-depth theoretical investigation of global attention mechanism,soft attention mechanism,and self-attention mechanism,and compared the advantages and disadvantages of these attention mechanisms in a comprehensive analysis.Based on these pros and cons,the Transformer model in the self-attention mechanism was chosen as the research object for the deblurring of superresolution image reconstruction.(2)This study proposed a novel VTCN network structure that combines the advantages of deep learning and prior knowledge to effectively eliminate blur while maintaining high-quality image reconstruction.The VTCN model design includes five modules: shallow feature extraction module,deblurring processing module,superresolution module,VTC module,and reconstruction module.The model utilizes residual blocks and dense connections to enhance the network’s ability to retain feature information,overcoming the issues of gradient vanishing or explosion.(3)This study conducted multiple experiments on the number of self-attention heads in the VTC module and finally determined that the model architecture with 16 heads is the most outstanding structure.The experiments used the GOPRO dataset,a commonly used dataset in the field of image deblurring,and applied data augmentation to the dataset.The experimental results show that the proposed VTCN network achieved excellent performance in the task of low-resolution blurry image reconstruction and has strong generalization capabilities.