A No-Reference Objective Image Sharpness Metric Based on Just-Noticeable Blur and Probability Summation

Author(s):  
Rony Ferzli ◽  
Lina J. Karam
2011 ◽  
Vol 1 (2) ◽  
pp. 83-87
Author(s):  
Chengho Hsin ◽  
Jr-Wei Jang ◽  
Shaw-Jyh Shin ◽  
Shin-Hsien Chen

2007 ◽  
Author(s):  
Eric P. Lam ◽  
Christopher A. Leddy ◽  
Stephen R. Nash

2017 ◽  
Vol 11 (3) ◽  
pp. 292-300 ◽  
Author(s):  
Jia-Wen Lin ◽  
Qian Weng ◽  
Lan-Yan Xue ◽  
Xin-Rong Cao ◽  
Lun Yu

Retinal image sharpness assessment is one of the critical requirements of automatic quality evaluation in telemedicine screening for diabetic retinopathy. In this paper, a new sharpness metric measuring the spread of edges is presented to quantify fundus image clarity. After edge detection on the region of interest of retinal image, the width of each edge is calculated and the histogram of region of interest generated. Based on the histogram, a distance-based factor is introduced to gain the weighted edge width, which is defined as the sharpness metric for the fundus image. The method was tested on Messidor dataset and a proprietary dataset. The results show that the proposed metric performs well over different image distortion levels and resolutions and is of low computational complexity. The weighted edge width value of gradable retinal image, which is irrelevant to resolution, is always within the range of 3–7 pixels.


2013 ◽  
Vol 20 (4) ◽  
pp. 379-382 ◽  
Author(s):  
C. Feichtenhofer ◽  
H. Fassold ◽  
P. Schallauer

2014 ◽  
Vol 902 ◽  
pp. 330-335
Author(s):  
Jian Ning Lai ◽  
Yi Gang Wang ◽  
Sheng Li Fan

Microscopic image sharpness metric is very sensitive to illumination changes, so the research of sharpness metric methods which have better robustness to illumination is required. A new method which utilized the specialty of different orientation of Local Binary Pattern (LBP) is proposed. Firstly, the effective information and judge orientation of them at fixed region of the image is extracted. Secondly, different connected pattern is adopted to deal with different orientation region and the most robust LBP image is gotten. Finally, the sharpness metric is measured from the LBP image. The experiment results show that this method can inhibit the influence of illumination to microscopic image sharpness metric, and the stability can be improved by 83.89% averagely and 99.90% maximum.


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