arXiv Analytics

Sign in

arXiv:1907.10128 [eess.IV]AbstractReferencesReviewsResources

Blind Deblurring using Deep Learning: A Survey

Siddhant Sahu, Manoj Kumar Lenka, Pankaj Kumar Sa

Published 2019-07-23Version 1

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.

Comments: 9 pages, 10 figures
Categories: eess.IV, cs.CV
Related articles: Most relevant | Search more
arXiv:1911.04357 [eess.IV] (Published 2019-11-11)
Limited View and Sparse Photoacoustic Tomography for Neuroimaging with Deep Learning
arXiv:2003.02920 [eess.IV] (Published 2020-03-02)
IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning
arXiv:2004.01607 [eess.IV] (Published 2020-04-03)
Cell Segmentation by Combining Marker-Controlled Watershed and Deep Learning