ResDeblur-GAN: A ResNet and PatchGAN Based Architecture for Image Deblurring

Convolutional deblurring is an advanced image restoration process that aims to recover sharp images from blurred ones caused by motion, defocus, or camera shake. This paper presents a deep learning approach leveraging DeblurGAN- v2, an improved generative adversarial network (GAN) archi- tecture. The model integrates a lightweight generator with a hierarchical discriminator and utilizes attention mechanisms and dense skip connections to retain fine image details. A novel loss function is introduced to balance perceptual, structural similarity, and adversarial components, reducing oversmoothing and enhancing restoration quality.