  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.

Computer Vision and Pattern Recognition;Image and Video Processing