  Blind motion deblurring involves reconstructing a sharp image from an
observation that is blurry. It is a problem that is ill-posed and lies in the
categories of image restoration problems. The training data-based methods for
image deblurring mostly involve training models that take a lot of time. These
models are data-hungry i.e., they require a lot of training data to generate
satisfactory results. Recently, there are various image feature learning
methods developed which relieve us of the need for training data and perform
image restoration and image synthesis, e.g., DIP, InGAN, and SinGAN. SinGAN is
a generative model that is unconditional and could be learned from a single
natural image. This model primarily captures the internal distribution of the
patches which are present in the image and is capable of generating samples of
varied diversity while preserving the visual content of the image. Images
generated from the model are very much like real natural images. In this paper,
we focus on blind motion deblurring through SinGAN architecture.
