Applications of edge preservation ratio in image processing

Abstract

Edge preservation ratio (EPR) is a full-reference metric for objective image quality assessment (IQA). It is under the assumption that key messages to human visual systems are mainly from image structures, and these structures can be extracted by edge detection. EPR measure is twofold: accuracy and robustness, and a color map is synthesized to reveal structure changes before and after image processing. The feasibility and superiority of EPR have been validated via image magnification and noise reduction. Experimental results suggest that: (1) it is challenging to fully recover lost messages by image magnification; (2) high image contrast may be derived from concise and distinct image structures.Index Terms-Edge preservation ratio, image quality assessment, image processing

Varying weighted spatial quality assessment for high resolution satellite image pan-sharpening

“…Edge Preservation Ratio (EPR): The general EPR method consists of accuracy and robustness components, the former refers to the ratio of properly preserved edge pixels in the distorted image to the number of edge pixels in the reference image, and the latter indicates the ratio between the number of preserved edge pixels to the number of edge pixels in the distorted image (Yu et al 2014).…”

Performance Evaluation of DBLA Technique Based on Image Enhancement by using Fuzzy Logic

“…To distinguish between corrupted and uncorrupted pixels two predetermined threshold values are elaborate in the computation of the second condition.Only pixels detection stage to be set for the noise in the next filtering stage. Yu et al(2014) [10] has provided that Edge preservation ratio (EPR) is a full-reference metric for objective image quality assessment(IQA).The probability and supremacy of EPR have now been validated via image amplification and noise decrease. Tentative effects propose it is tough to totally recover missing communications by image zoom and high image distinction may be produced from brief and distinctive image assemblies.…”

“…Enhance its adaptability and effectiveness in a gray scale image detail. Jin et al(2015) [14] has offered a new method for both noise suppression and edge protection. To perceive the edge info the building tensor is proposed.…”