Edge Preserving Range Image Smoothing by Rotated Bilateral Sampling

2015 ◽  
Author(s):  
Viktor Kovacs ◽  
Kristof Csorba ◽  
Gabor Tevesz
Keyword(s):  
Range Image ◽  
Image Smoothing ◽  
Edge Preserving

Dense range image smoothing using adaptive regularization

2000 ◽  
Author(s):  
Yiyong Sun ◽  
Joon-Ki Paik ◽  
J.R. Price ◽  
M.A. Abidi
Keyword(s):  
Range Image ◽  
Image Smoothing ◽  
Adaptive Regularization ◽  
Dense Range

Real-Time Bilateral Filtering Using GPGPU

2015 ◽  
Vol 781 ◽  
pp. 568-571 ◽  
Author(s):  
Sanun Srisuk ◽  
Wachirapong Kesjindatanawaj ◽  
Surachai Ongkittikul
Keyword(s):  
Nonlinear Filtering ◽  
Computation Time ◽  
General Purpose ◽  
Experimental Results ◽  
Bilateral Filtering ◽  
Image Smoothing ◽  
Edge Preserving ◽  
Filter Kernel ◽  
Sharp Edges ◽  
General Purpose Gpu

In this paper, we present a technique for accelerating the bilateral filtering using GPGPU. Bilateral filtering is a tool for an image smoothing with edge preserving properties. It serves as a mixture of domain and range filters. Domain filter suppresses Gaussian noise while range filter maintains sharp edges. Bilateral filtering is a nonlinear filtering in which the filter kernel must be computed pixel by pixel. Therefore conventional fast Fourier transform technique cannot be used to accelerate the bilateral filtering. Instead, general purpose GPU is used as a parallel machine to reduce time consuming of the bilateral filtering. We will show the experimental results by comparing the computation time of CPU and GPU. It was cleared that, from the experimental results, GPU outperformed the CPU in terms of computation time.

The effect of edge-preserving image smoothing on automatic colonic polyp detection for CT colonography

2006 ◽  
Author(s):  
David Pilkinton ◽  
Ingmar Bitter ◽  
Ronald M. Summers ◽  
Shannon Campbell ◽  
J. R. Choi ◽  
...  
Keyword(s):  
Ct Colonography ◽  
Colonic Polyp ◽  
Image Smoothing ◽  
Polyp Detection ◽  
Edge Preserving ◽  
Colonic Polyp Detection

scholarly journals A Generalized Framework for Edge-Preserving and Structure-Preserving Image Smoothing

2020 ◽  
Vol 34 (07) ◽  
pp. 11620-11628
Author(s):  
Wei Liu ◽  
Pingping Zhang ◽  
Yinjie Lei ◽  
Xiaolin Huang ◽  
Jie Yang ◽  
...  
Keyword(s):  
Penalty Function ◽  
State Of The Art ◽  
Superior Performance ◽  
Image Smoothing ◽  
Edge Preserving ◽  
Structure Preserving ◽  
Smoothing Operator ◽  
Optimization Framework ◽  
Generalized Framework ◽  
Smooth Optimization

Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various requirements of different applications. In this paper, a non-convex non-smooth optimization framework is proposed to achieve diverse smoothing natures where even contradictive smoothing behaviors can be achieved. To this end, we first introduce the truncated Huber penalty function which has seldom been used in image smoothing. A robust framework is then proposed. When combined with the strong flexibility of the truncated Huber penalty function, our framework is capable of a range of applications and can outperform the state-of-the-art approaches in several tasks. In addition, an efficient numerical solution is provided and its convergence is theoretically guaranteed even the optimization framework is non-convex and non-smooth. The effectiveness and superior performance of our approach are validated through comprehensive experimental results in a range of applications.

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