Dynamic scene Image deblurring using modified scale-recurrent network

In recent times, the non-uniform blind deblurring for dynamic scenes is considered as one of the severely ill-posed challenges in computer vision because the blur is not only caused from various different object motions, and also it will come from the shake of the camera and also from scene depth variation. To get rid of the complex motion blurs, the conventional energy optimization technique that depends on some simple assumptions such that the blur kernels are partially uniform and as well as locally linear. Furthermore, in present days the machine learning (ML) construct approach will depend on synthetic blur dataset made down with this kind of assumptions. Hence, this makes conventional deblurring technique to grief-in-order to eliminate the complicated blurs, where the blur kernel itself is hard to parameterized or approximated [ex: different objects motion boundaries], So in the proposed experiment the multi-scale strategy called scale recurrent network (SRN), that restores the sharp images in an end-to-end manner is employed, where the blur is originating from different sources. When it is compared with some recent learning based approaches, the SRN-Deblur Net has the simpler network structures and it has compact number of parameters and simpler to train the network and the proposed approach is analyzed on the large-scale deblurring datasets with the complicated motions and thus the result showed that the use of modified SNR-Deblur Net can produce a better quality of latent sharp images, that remains state-of-the-art in both quantitatively and qualitatively.