${\bf S}_{3}$: A Spectral and Spatial Measure of Local Perceived Sharpness in Natural Images

Abstract

This paper presents an algorithm designed to measure the local perceived sharpness in an image. Our method utilizes both spectral and spatial properties of the image: For each block, we measure the slope of the magnitude spectrum and the total spatial variation. These measures are then adjusted to account for visual perception, and then, the adjusted measures are combined via a weighted geometric mean. The resulting measure, i.e., S(3) (spectral and spatial sharpness), yields a perceived sharpness map in which greater values denote perceptually sharper regions. This map can be collapsed into a single index, which quantifies the overall perceived sharpness of the whole image. We demonstrate the utility of the S(3) measure for within-image and across-image sharpness prediction, no-reference image quality assessment of blurred images, and monotonic estimation of the standard deviation of the impulse response used in Gaussian blurring. We further evaluate the accuracy of S(3) in local sharpness estimation by comparing S(3) maps to sharpness maps generated by human subjects. We show that S(3) can generate sharpness maps, which are highly correlated with the human-subject maps.

Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization

“…Compared with other NR IQA algorithms [48][49][50][51], LPC-SI correlates better with image sharpness and shows less fluctuation with the iterations. Its computational load is also acceptable.…”

“…NR IQA indexes aim to predict the human evaluation of image quality, without any access to a reference image. During the past decade, lots of NR IQA algorithms [48][49][50][51][52] have been proposed to evaluate the sharpness/blurring of images. Some of the indexes have been proven to be effective in capturing the blurring effect on the perceived sharpness, and can even achieve comparative results to some full-reference (FR) IQA indexes, which are usually more accurate.…”

Automatic Boat Identification System for VIIRS Low Light Imaging Data

“…The objective is to identify detections that are impacted by clouds. After testing several blur detection options we settled on the S3 Spectral Measure of Sharpness algorithm [17]. S3 is based on a spectral measure of the slope of the local spatial spectrum of the image magnitude.…”

Saliency Guided Gradient Similarity for Fast Perceptual Blur Assessment

“…Later, JNB was improved to the model of cumulative probability of blur detection (CPBD) [6]. Vu et al [7] proposed the S3 metric, which measures image blur in both spectral and spatial domains. The attenuation of high-frequency components was measured using the slope of local magnitude spectrum, and the impact of local contrast was measured by total variation.…”