Paper Contents
Abstract
AbstractConvolutional 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.
Copyright
Copyright © 2025 Pavan Mutyala. This is an open access article distributed under the Creative Commons Attribution License.
