A dialectical analysis of non-reference image quality measures (IQMs) and restoration filters for single image blind deblurring

Blind image deblurring relies on a good estimation of Point Spread Function (PSF) and the utilization of an effective restoration filter. Even if the PSF is estimated well, the deblurring result depends heavily on the abilities of the restoration filter to produce a good approximation of the pristine image. Blind Image Quality Measures (IQMs) that guide the deblurring algorithm are also dependent on the restored image data. This research work evaluated the performance of the restoration filters and the blind IQMs when the true PSF has been estimate dand presents the effectiveness of both for blind deblurring. Wiener, Richardson-Lucy and Total Variation deblurring filters and BRISQUE, NIQE, SSEQ, Curvelet QA (CQA) IQMs have been analysed. Simulations have been performed over a wide range of images various blurring types (Gaussian, out-of-focus and motion). The results show that TV deblurring filter in conjunction with CQA deliver a near estimate of the pristine image for the artifically blurred images. In the case of real blurred images, Wiener filter presents high quality deblurred images and both SSEQ and CQA depict high quality images.