Halftoning without dither or edge enhancement

1991 ◽  
Vol 7 (5-6) ◽  
pp. 232-246 ◽  
Author(s):  
A. J. Cole
Keyword(s):  
Edge Enhancement

scholarly journals Extraction of Blood Vessels in Retinal Images Using Four Different Techniques

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Asloob Ahmad Mudassar ◽  
Saira Butt
Keyword(s):  
Blood Vessel ◽  
Cross Sections ◽  
Retinal Images ◽  
Matched Filtering ◽  
Edge Enhancement ◽  
Promising Technique ◽  
Filtering Technique ◽  
Continuation Algorithm ◽  
Line Cross ◽  
Vessel Extraction

A variety of blood vessel extraction (BVE) techniques exist in the literature, but they do not always lead to acceptable solutions especially in the presence of anomalies where the reported work is limited. Four techniques are presented for BVE: (1) BVE using Image Line Cross-Sections (ILCS), (2) BVE using Edge Enhancement and Edge Detection (EEED), (3) BVE using Modified Matched Filtering (MMF), and (4) BVE using Continuation Algorithm (CA). These four techniques have been designed especially for abnormal retinal images containing low vessel contrasts, drusen, exudates, and other artifacts. The four techniques were applied to 30 abnormal retinal images, and the success rate was found to be (95 to 99%) for CA, (88–91%) for EEED, (80–85%) for MMF, and (74–78%) for ILCS. Application of these four techniques to 105 normal retinal images gave improved results: (99-100%) for CA, (96–98%) for EEED, (94-95%) for MMF, and (88–93%) for ILCS. Investigations revealed that the four techniques in the order of increasing performance could be arranged as ILCS, MMF, EEED, and CA. Here we demonstrate these four techniques for abnormal retinal images only. ILCS, EEED, and CA are novel additions whereas MMF is an improved and modified version of an existing matched filtering technique. CA is a promising technique.

Noncausal error-diffusion method for edge enhancement

1997 ◽  
Author(s):  
Ki-Min Kang ◽  
Choon-Woo Kim
Keyword(s):  
Diffusion Method ◽  
Error Diffusion ◽  
Edge Enhancement

Non-linear polynomial filters for edge enhancement of mammogram lesions

2016 ◽  
Vol 129 ◽  
pp. 125-134 ◽  
Author(s):  
Vikrant Bhateja ◽  
Mukul Misra ◽  
Shabana Urooj
Keyword(s):  
Edge Enhancement ◽  
Linear Polynomial ◽  
Non Linear ◽  
Polynomial Filters

A general framework for quadratic Volterra filters for edge enhancement

1996 ◽  
Vol 5 (6) ◽  
pp. 950-963 ◽  
Author(s):  
S. Thurnhofer ◽  
S.K. Mitra
Keyword(s):  
General Framework ◽  
Edge Enhancement ◽  
Volterra Filters

Edge Enhancement Nucleus and Cytoplast Contour Detector of Cervical Smear Images

2008 ◽  
Vol 38 (2) ◽  
pp. 353-366 ◽  
Author(s):  
Shys-Fan Yang-Mao ◽  
Yung-Kuan Chan ◽  
Yen-Ping Chu
Keyword(s):  
Edge Enhancement ◽  
Cervical Smear

Image denoising via deep network based on edge enhancement

2018 ◽  
Author(s):  
Xi Chen ◽  
Shu Zhan ◽  
Dong Ji ◽  
Liangfeng Xu ◽  
Congzhong Wu ◽  
...  
Keyword(s):  
Image Denoising ◽  
Edge Enhancement ◽  
Deep Network

Edge enhancement of light emission from Au particles induced by an STM

1997 ◽  
Vol 234 (5) ◽  
pp. 396-400 ◽  
Author(s):  
Z.H. Wu ◽  
T. Nakayama ◽  
M. Sakurai ◽  
M. Aono
Keyword(s):  
Light Emission ◽  
Edge Enhancement

Gaussian derivative model for edge enhancement

1994 ◽  
Vol 27 (11) ◽  
pp. 1451-1461 ◽  
Author(s):  
Mitra Basu
Keyword(s):  
Edge Enhancement ◽  
Gaussian Derivative
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