  In this paper, we propose to reformulate the blind image deblurring task to
directly learn an inverse of the degradation model using a deep linear network.
We introduce Deep Identity Learning (DIL), a novel learning strategy that
includes a dedicated regularization term based on the properties of linear
systems, to exploit the identity relation between the degradation and inverse
degradation models. The salient aspect of our proposed framework is it neither
relies on a deblurring dataset nor a single input blurred image (like Polyblur,
a self-supervised method). Since it is purely image-data-independent, we term
our model as Null-Shot deblurring Using Deep Identity Learning (NSD-DIL). We
also provide an explicit representation of the learned deep linear network in a
matrix form, called Deep Restoration Kernel (DRK) for deblurring task. The
proposed framework detours the typical degradation kernel estimation step
involved in most of the existing blind deblurring solutions by the proposition
of our Random Kernel Gallery (RKG) dataset. In this work, we focus on the
restoration of mild blur images, generated by small out-of-focus, lens blur, or
slight camera motion, which often occurs in real images. Our experiments show
that the proposed method outperforms both traditional and deep learning based
deblurring methods, with at least an order of 100 lesser computational
resources. The proposed NSD-DIL method can be effortlessly extended to the
Image Super-Resolution (ISR) task as well to restore the low-resolution images
with fine details. The NSD-DIL model and its kernel form representation (DRK)
are lightweight yet robust and restore the mild blur input in a fraction of a
second. Hence, more suitable for wide real-time applications.
