Blur Estimation of Blind Image Using Deep Convolutional Neural Networks

Abstract

Images have become an integral part of our daily lives, in social networking, in scientific applications or surveillance systems and where there is image, comes the concept of blurring. Blurring of a picture may be a major explanation for image degradation which may be caused by various blurs namely, Gaussian blur, motion blur, defocus blur, average blur or threshold blur. Thereby, the proposed system aims at performing blur classification, estimation of parameters and deblurring in a three stage framework through deep learning. Firstly, the proposed system identifies the blur type from a mixed input of images i.e. black and white or color image degraded by various blurs with different parameters using a pre-trained deep neural Network (DNN) in a supervised way. The robustness, effectiveness, efficiency and competency of the proposed system shall be noted and accordingly applied to real world scenarios to demonstrate the same.