Image Deblurring using GAN

TL;DR

This paper presents a GAN-based framework for image deblurring that effectively generates sharper images from motion-blurred inputs, outperforming traditional methods in quality metrics like PSNR and SSIM.

Contribution

It introduces a novel GAN model trained on the GoPRO dataset specifically for motion deblurring, demonstrating improved image sharpness and quality over existing techniques.

Findings

Achieved an average PSNR of 29.3 for deblurred images

Obtained a SSIM of 0.72 indicating high structural similarity

Successfully applied the model to real-world motion-blurred images

Abstract

In recent years, deep generative models, such as Generative Adversarial Network (GAN), has grabbed significant attention in the field of computer vision. This project focuses on the application of GAN in image deblurring with the aim of generating clearer images from blurry inputs caused by factors such as motion blur. However, traditional image restoration techniques have limitations in handling complex blurring patterns. Hence, a GAN-based framework is proposed as a solution to generate high-quality deblurred images. The project defines a GAN model in Tensorflow and trains it with GoPRO dataset. The Generator will intake blur images directly to create fake images to convince the Discriminator which will receive clear images at the same time and distinguish between the real image and the fake image. After obtaining the trained parameters, the model was used to deblur motion-blur images taken in daily life as well as testing set for validation. The result shows that the pretrained network of GAN can obtain sharper pixels in image, achieving an average of 29.3 Peak Signal-to-Noise Ratio (PSNR) and 0.72 Structural Similarity Assessment (SSIM). This help to effectively address the challenges posed by image blurring, leading to the generation of visually pleasing and sharp images. By exploiting the adversarial learning framework, the proposed approach enhances the potential for real-world applications in image restoration.