Perceptual quality evaluation for motion deblurring

Abstract

Motion deblurring has been widely studied. However, the relevant quality evaluation of motion deblurred images remains an open problem. The motion deblurred images are usually contaminated by noise, ringing and residual blur (NRRB) simultaneously. Unfortunately, most of the existing quality metrics are not designed for multiply distorted images, so they are limited in predicting the quality of motion deblurred images. In this study, the authors propose a new quality metric for motion deblurred images by measuring NRRB. For a motion deblurred image, the noise level is first estimated. Then the ringing effect is measured by incorporating visual saliency model to adapt to the characteristic of the human visual system. A reblurring-based method is proposed to extract similarity features between a motion deblurred image and its re-blurred version for evaluating the residual blur. Finally, the overall quality score of a motion deblurred image is obtained by pooling the scores of noise, ringing and blur. Experimental results conducted on a motion deblurring database demonstrate that the proposed metric significantly outperforms the existing quality metrics. In addition, the proposed NRRB metric is used for improving the existing generalpurpose no-reference metrics, and very encouraging results are achieved.

Blind image deblurring for a close scene under a 6-DOF motion path

“…No-reference structural sharpness (NRSS) is proportional to image quality (Leida Li et al , 2017; Bo Hu et al , 2018; Sathya Veera Reddy Dendi et al , 2019). This method performs low-pass filtering on the image to be evaluated to construct a reference image, and evaluates the clarity of the image by calculating the structural similarity between the constructed reference image and the image to be evaluated.…”