Noise image compressed coding based on edge detection

2009 ◽  
Vol 28 (9) ◽  
pp. 2297-2299
Author(s):  
Ying-hua TIAN ◽  
Jing-song YANG ◽  
Yue TAO
Keyword(s):  
Edge Detection ◽  
Noise Image

Heuristic Search with a New Operator Based Krisch Combination of Image Edge Detection

2012 ◽  
Vol 461 ◽  
pp. 461-465
Author(s):  
Jian Ni ◽  
Yu Duo Li
Keyword(s):  
Edge Detection ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Small Scale ◽  
Edge Detector ◽  
Heuristic Search Algorithm ◽  
Noise Image ◽  
Speed Advantage ◽  
Image Edge ◽  
Local Average

This paper designed a new edge detection[1,3] operator based on Krisch edge detector and direction encoding and combining the local average, while proposed heuristic search algorithm in the noise image based on segmentation edge of the self-enhanced. First, use small-scale Gaussian filtering in the noise image, and then use this edge detector for edge detection, and trajectory of each sub-search self-reinforcing, and finally according to the degree of self-reinforcing accumulation of noise to obtain the image edge. The results show that: This algorithm can effectively extract the real object from the noise, and to maximize the retention of details. And the performance better than Krisch and has some speed advantage

Edge Detection for Noise Image by Wavelet Transform and Mathematical Morphology

2011 ◽  
Vol 121-126 ◽  
pp. 4441-4445
Author(s):  
Hai Long Huang ◽  
Hong Wang
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Mathematical Morphology ◽  
Linear Structure ◽  
Detection Algorithm ◽  
Good Time ◽  
Detection Accuracy ◽  
Edge Detection Algorithm ◽  
Time Frequency ◽  
Noise Image

It is much more complex and difficult for edge detection of noise image compared to edge detection of normal image，the analysis and study of edge detection of noise image has universal significance and practical value. Wavelet transform possesses good time-frequency localization characteristic and multi-scale analytical ability, mathematical morphology is a new subject based on set theory, which is very suitable for analyzing and describing geometrical feature of signal. Combining the advantages of wavelet transform and mathematical morphology, the paper proposes an edge detection algorithm, which mainly focused on noise image. For edge detection based on mathematical morphology, constructs an anti-noise operator of edge detection by improving existing operators and employs different direction linear structure elements; edge detection based on mathematical morphology can reserve details of edge effectively, ensure the continuity and integrity of edge detected. Experimental results show the proposed algorithm can suppress the interference of different density and different types of noise more effectively in comparison with several classical edge detection algorithm, thus improving the detection accuracy and robustness for different images.

Difference mode EXELFS with parallel-detection EELS

1992 ◽  
Vol 50 (2) ◽  
pp. 1262-1263
Author(s):  
Michael K. Kundmann ◽  
Ondrej L. Krivanek
Keyword(s):  
Edge Detection ◽  
Important Distinction ◽  
Edge Structure ◽  
Parallel Detection ◽  
Gain Variation ◽  
Ideal Solutions ◽  
Detector Cell ◽  
The Difference ◽  
Elemental Detection ◽  
Partial Correction

Parallel detection has greatly improved the elemental detection sensitivities attainable with EELS. An important element of this advance has been the development of differencing techniques which circumvent limitations imposed by the channel-to-channel gain variation of parallel detectors. The gain variation problem is particularly severe for detection of the subtle post-threshold structure comprising the EXELFS signal. Although correction techniques such as gain averaging or normalization can yield useful EXELFS signals, these are not ideal solutions. The former is a partial throwback to serial detection and the latter can only achieve partial correction because of detector cell inhomogeneities. We consider here the feasibility of using the difference method to efficiently and accurately measure the EXELFS signal.An important distinction between the edge-detection and EXELFS cases lies in the energy-space periodicities which comprise the two signals. Edge detection involves the near-edge structure and its well-defined, shortperiod (5-10 eV) oscillations. On the other hand, EXELFS has continuously changing long-period oscillations (∼10-100 eV).

A Dividing Ratio Changeable Digital PLL Based on Phase State Memory and Double Clock-Edge Detection

2008 ◽  
Vol 128 (7) ◽  
pp. 1185-1190 ◽  
Author(s):  
Kuniaki Fujimoto ◽  
Hirofumi Sasaki ◽  
Mitsutoshi Yahara
Keyword(s):  
Edge Detection ◽  
Phase State
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