scholarly journals Coherence Enhancement Based on Recursive Anisotropic Scale-Space with Adaptive Kernels

2020 ◽  
Vol 10 (15) ◽  
pp. 5079
Author(s):  
Numonov Sardorbek ◽  
Bong-Soo Sohn ◽  
Byung-Woo Hong
Keyword(s):  
Diffusion Process ◽  
Image Denoising ◽  
Geometrical Property ◽  
Scale Space ◽  
Image Features ◽  
Diffusion Kernel ◽  
Geometrical Properties ◽  
Curvilinear Structures ◽  
Lung Ct ◽  
Space Image

The reduction of unnecessary details is important in a variety of imaging tasks. Image denoising can be generally formulated as a diffusion process that iteratively suppresses undesirable image features with high variance. We propose a recursive diffusion process that simultaneously computes the local geometrical property of the image features and determines the size and shape of the diffusion kernel, leading to an anisotropic scale-space. In the construction of the proposed anisotropic scale-space, image features due to undesirable noise are suppressed while significant geometrical image features such as edges and corners are preserved across the scale-space. The diffusion kernels are iteratively determined based on the local geometrical properties of the image features. We demonstrate the effectiveness and robustness of the proposed algorithm in the detection of curvilinear features using simple yet effective synthetic and real images. The algorithm is quantitatively evaluated based on the identification of fissures in lung CT imagery. The experimental results indicate that the proposed algorithm can be used for the detection of linear or curvilinear structures in a variety of images ranging from satellite to medical images.

A new approach to scale-space image

1994 ◽  
Vol 15 (4) ◽  
pp. 365-372 ◽  
Author(s):  
Bimal Kumar Ray ◽  
Kumar S. Ray
Keyword(s):  
Scale Space ◽  
New Approach ◽  
Space Image

Image features using scale-space FAST corner detector and SURF descriptor

2014 ◽  
Vol 29 (4) ◽  
pp. 598-604
Author(s):  
王飞宇 WANG Fei-yu ◽  
邸男 DI Nan ◽  
贾平 JIA Ping
Keyword(s):  
Scale Space ◽  
Image Features

scholarly journals Stochastic analysis of image acquisition, interpolation and scale-space smoothing

1999 ◽  
Vol 31 (4) ◽  
pp. 855-894 ◽  
Author(s):  
Kalle Åström ◽  
Anders Heyden
Keyword(s):  
Stochastic Analysis ◽  
Traditional Approach ◽  
Image Acquisition ◽  
Scale Space ◽  
Image Features ◽  
Space Theory ◽  
Random Errors ◽  
Feature Detectors ◽  
Stochastic Properties ◽  
Discrete Scale

In the high-level operations of computer vision it is taken for granted that image features have been reliably detected. This paper addresses the problem of feature extraction by scale-space methods. There has been a strong development in scale-space theory and its applications to low-level vision in the last couple of years. Scale-space theory for continuous signals is on a firm theoretical basis. However, discrete scale-space theory is known to be quite tricky, particularly for low levels of scale-space smoothing. The paper is based on two key ideas: to investigate the stochastic properties of scale-space representations and to investigate the interplay between discrete and continuous images. These investigations are then used to predict the stochastic properties of sub-pixel feature detectors.The modeling of image acquisition, image interpolation and scale-space smoothing is discussed, with particular emphasis on the influence of random errors and the interplay between the discrete and continuous representations. In doing so, new results are given on the stochastic properties of discrete and continuous random fields. A new discrete scale-space theory is also developed. In practice this approach differs little from the traditional approach at coarser scales, but the new formulation is better suited for the stochastic analysis of sub-pixel feature detectors.The interpolated images can then be analysed independently of the position and spacing of the underlying discretisation grid. This leads to simpler analysis of sub-pixel feature detectors. The analysis is illustrated for edge detection and correlation. The stochastic model is validated both by simulations and by the analysis of real images.

scholarly journals An adaptive bidirectional diffusion process for passive millimeter-wave image denoising and enhancement

2012 ◽  
Vol 30 (6) ◽  
pp. 556-560 ◽  
Author(s):  
Jin-Tao XIONG ◽  
Qian-Song SUN ◽  
Liang-Chao LI ◽  
Jian-Yu YANG
Keyword(s):  
Diffusion Process ◽  
Image Denoising ◽  
Millimeter Wave ◽  
Wave Image

scholarly journals Image Scale-Space Filtering Using Directional Local Variance Controlled Anisotropic Diffusion

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yong Chen ◽  
Taoshun He
Keyword(s):  
Anisotropic Diffusion ◽  
Scale Space ◽  
Image Features ◽  
Spatial Gradient ◽  
Trigger Time ◽  
Local Variance ◽  
Series Of Experiments ◽  
Edge Indicator ◽  
Anisotropic Diffusion Equation ◽  
Space Filter

The purpose of this paper is to develop an effective edge indicator and propose an image scale-space filter based on anisotropic diffusion equation for image denoising. We first develop an effective edge indicator named directional local variance (DLV) for detecting image features, which is anisotropic and robust and able to indicate the orientations of image features. We then combine two edge indicators (i.e., DLV and local spatial gradient) to formulate the desired image scale-space filter and incorporate the modulus of noise magnitude into the filter to trigger time-varying selective filtering. Moreover, we theoretically show that the proposed filter is robust to the outliers inherently. A series of experiments are conducted to demonstrate that the DLV metric is effective for detecting image features and the proposed filter yields promising results with higher quantitative indexes and better visual performance, which surpass those of some benchmark models.

scholarly journals Research on Sports Training Action Image Recognition Based on SDN

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Dianhai Wang ◽  
Lianmei Shen
Keyword(s):  
Image Denoising ◽  
Image Recognition ◽  
Network Architecture ◽  
Image Data ◽  
Image Features ◽  
Test Results ◽  
Application Layer ◽  
Sports Training ◽  
Detection Range ◽  
Image Edge

Current image recognition methods cannot combine the transmission of image data with the interaction of image features, so the steps of image recognition are too independent, and the traditional methods take longer time and cannot complete the image denoising. Therefore, a recognition method of sports training action image based on software defined network (SDN) architecture is proposed. The SDN architecture is used to integrate the image data transmission and interactive process and to optimize the image processing centralization. The network architecture is composed of application layer, control layer, and infrastructure layer. Based on this, the dimension of image sample set is reduced, and the edge detection operator in any direction is constructed. The image edge filter is realized by calculating the response and threshold of image edge by using lag threshold and nonmaximum suppression (NMS). The Hough transform algorithm is improved to optimize the detection range. Extracting the neighborhood feature of sports training action, the recognition of sports training action image based on SDN architecture is completed. Simulation results show that the proposed method takes less time and the image denoising effect is better. In addition, the F1 test results of the proposed method are higher than those of the literature, and the convergence is better. Therefore, the performance of the proposed method is better.

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