Adaptive rank-conditioned median filter for edge-preserving image smoothing

1996 ◽  
Author(s):  
Luciano Alparone ◽  
Stefano Baronti ◽  
Roberto Carla
Keyword(s):  
Median Filter ◽  
Image Smoothing ◽  
Edge Preserving

scholarly journals Architectural Design of Effective Edge Preserving Median Filter with Error Correction System

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4057-4067
Keyword(s):  
Architectural Design ◽  
Median Filter ◽  
Soft Error ◽  
Data Driven ◽  
Switching Loss ◽  
Clock Gating ◽  
Edge Preservation ◽  
Memory State ◽  
Edge Preserving ◽  
Modular Redundancy

Designing of Median filter that can process 36 pixels at a time with edge preservation similar to a filter of size 9. Median sorting is done using Modified minimum exchange sorting method which attracts double the amount of inputs in order to reduce number of comparators used for median filtering. For the same reason i.e. double the amount of inputs switching loss is high in the circuit therefore data driven clock gating (DDCG) is applied for SRAM to form data driven FIFO. Considering space radiation that could excite memory state, Addition of DMR (Double Modular Redundancy) in FPIC would rectify the soft error that could possibly occur due to radiation in space. Therefore proposed method is capable of producing sharp image, controlling switching loss, minimizes area, and reduces soft errors.

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.

scholarly journals A study of edge preserving filters in image matching

2021 ◽  
Vol 10 (1) ◽  
pp. 111-117
Author(s):  
Rostam Affendi Hamzah ◽  
A. F. Kadmin ◽  
S. F. A. Gani ◽  
K. A. Aziz ◽  
T. M. F. T. Wook ◽  
...  
Keyword(s):  
Image Matching ◽  
Evaluation System ◽  
Stereo Matching ◽  
Median Filter ◽  
Depth Map ◽  
Bilateral Filter ◽  
Low Noise ◽  
Disparity Map ◽  
Edge Preserving ◽  
Accurate Matching

This article presents a study on edge preserving filters in image matching which comprises a development of stereo matching algorithm using two edge preserving filters. Fundamentally, the framework is reconstructed by several sequential processes. The output of these processes is a disparity map or depth map. The corresponding points between two images require accurate matching to make accurate depth map estimation. Thus, the propose work in this article utilizes sum of squared differences (SSD) with dual edge preserving filters. These filters are used due to edge preserved properties and to increase the accuracy. The median filter (MF) and bilateral filter (BF) will be utilized. The SSD produces preliminary results with low noise and the edge preserving filters reduce noise on the low texture regions with edge preserving properties. Based on the experimental analysis using the standard benchmarking evaluation system from the Middlebury, the disparity map produced is 6.65% for all error pixels. It shows an accurate edge preserved properties on the disparity maps. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it displays the proposed work in this article perform much better.

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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