No-Reference Blur Assessment Based on Entropy and PCNN for Restoration Images

Abstract

In practical problems, there are usually no clear counterparts as reference to evaluate restoration results. So no-reference blur assessment is very important and necessary. In this paper, we proposed an objective measure named as Edge Factor (EF) to appraise image blurring. The fundamental rationale was that blurring effect was much more perceptible in edge transition zones. The pixel number of edge transition zones would decrease when blurring occured. We defined the pixel number ratio of the edge transition zones to the whole image as EF. Experimental results show the monotonic consistency of EF and RMS. The proposed method is further compared with some common edge detection algorithms to demonstrate the effectiveness of combining point-based entropy with Pulse Coupled Neural Network.