  We inspect all the deep learning based solutions and provide holistic
understanding of various architectures that have evolved over the past few
years to solve blind deblurring. The introductory work used deep learning to
estimate some features of the blur kernel and then moved onto predicting the
blur kernel entirely, which converts the problem into non-blind deblurring. The
recent state of the art techniques are end to end, i.e., they don't estimate
the blur kernel rather try to estimate the latent sharp image directly from the
blurred image. The benchmarking PSNR and SSIM values on standard datasets of
GOPRO and Kohler using various architectures are also provided.
