Motion Blur Detection Using Deep Generative Adversarial Network Method

Abstract

This paper presents a novel approach for blur detection and removal using Generative Adversarial Networks (GANs). The proposed method leverages the power of deep learning to automatically identify and eliminate blur in digital images. The first phase of the process involves training a GAN model on a dataset of paired images, where one image is sharp and the other is intentionally blurred. The GAN consists of a generator network that aims to generate sharp images from their blurred counterparts, and a discriminator network that distinguishes between real and generated sharp images. During the training phase, the generator network learns the mapping from blurred images to sharp images, while the discriminator network improves its ability to differentiate between real and generated sharp images. This adversarial training process helps the GAN model improve its performance in detecting and removing blur from images. In the testing phase, the trained GAN model can be used to enhance images by detecting and effectively removing blur. Experimental results demonstrate the effectiveness of the proposed approach in achieving high-quality image restoration and enhancement. Overall, the proposed blur detection and removal technique and removal technique using GANs showcases the potential of deep learning in addressing challenging image processing tasks and contributes to the ongoing advancements in the field of computer vision.