Presenting an attention-based hybrid generative network for image resolution enhancement
Improving the quality of images in the field of computer vision has been raised as one of the keychallenges. In this paper, a new attention-based super-resolution adversarial generative neuralnetwork model is introduced, which is developed by combining real image resolution enhancementnetwork models and residual channel-based attention network to improve reconstruction of highresolutionimages and reduce complex distortions and noises. Image distortions include geometricdistortions, blurring, and loss of detail commonly seen in low-resolution or compressed images. Inthis model, the capabilities of the real-world image enhancement network to reduce noise andenhance clarity along with the ability of the residual channel-oriented attention network to preserveFine details are used. Experimental results on several examples of well-known data sets in this fieldhave shown that the proposed model improves the performance by about 5% on average in PIQEand . compared to existing methods such as attention enhancement network and real imageresolution enhancement network in well-known measures of blind image reconstruction. Theseimprovements have enabled the model to reconstruct images with high resolution and minimaldistortion and noise.