An improved perception-based no-reference objective image sharpness metric using iterative edge refinement
The computation of existing sharpness/blurriness objective metrics involves measuring the spread of edge pixels in blurred images. However, in blurred images, many edges might go undetected causing the metrics to become inaccurate. In these scenarios, proper recovery of edge pixels can lead to a better correlation between the perceived sharpness and the sharpness metric. This paper presents an iterative edge refinement algorithm. The proposed edge refinement scheme is integrated into a perceptual- based no-reference sharpness metric resulting in an increased correlation with the perceived sharpness and, thus, in an increased sharpness/blurriness prediction accuracy. Results are presented to illustrate the performance of the proposed scheme and metric.