FC-SRGAN: fuzzy convolutional super restoration generative adversarial network-based super resolution for image restoration

This research introduced a novel hybrid framework using a Fuzzy Convolutional-Super Resolution Generative Adversarial Network (FC-SRGAN) for the restoration of images. The input image is first obtained from a predefined dataset. The Deep Kronecker Network (DKN) is then employed to identify noise pixels, while a statistical model is used to eliminate the unwanted noise. Then image inpainting is accomplished using Context-Conditional Generative Adversarial Networks (CC-GAN) combined with an encoder optimized by the Jaya Waterwheel Plant Algorithm (JWWPA). The JWWPA is the fusion of Jaya optimization and the Waterwheel Plant Algorithm (WWPA). Finally, image restoration is done using the FC-SRGAN. The developed FC-SRGAN method obtained the highest value for Peak Signal-to-Noise Ratio (PSNR) as 38.16 dB, the Second-Derivative-like Measure of Enhancement (SDME) as 59.70 dB, the Structural Similarity Index (SSIM) as 0.799, Universal Quality Index (UQI) as 0.856, and the Figure of Merit (FOM) as 0.982.