Progressive decision-based mean type filter for image noise suppression

Authors:
Tzu-Chao Lin
Affiliations:
Department of Computer Science and Information Engineering, WuFeng Institute of Technology, Chiayi, Taiwan 62107, ROC
Venue:
Computer Standards & Interfaces
Year:
2008

Citing 10
Cited 2

Detail-preserving median based filters in image processing

Pattern Recognition Letters
Median filter based on fuzzy rules and its application to image restoration

Fuzzy Sets and Systems - Special issue on fuzzy signal processing
Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account

Pattern Recognition Letters
Adaptive Two-Pass Median Filter Based on Support Vector Machines for Image Restoration

Neural Computation
A signal detection system based on Dempster-Shafer theory andcomparison to fuzzy detection

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Type-2 fuzzy logic systems

IEEE Transactions on Fuzzy Systems
Type-2 fuzzy sets made simple

IEEE Transactions on Fuzzy Systems
A noise-filtering method using a local information measure

IEEE Transactions on Image Processing
Tri-state median filter for image denoising

IEEE Transactions on Image Processing
Selective removal of impulse noise based on homogeneity level information

IEEE Transactions on Image Processing

Decision-based fuzzy image restoration for noise reduction based on evidence theory

Expert Systems with Applications: An International Journal
Partition-based fuzzy median filter based on adaptive resonance theory

Computer Standards & Interfaces

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel progressive decision-based mean (PDM) filter is proposed to restore images corrupted by random-valued impulse noise. An impulse detection algorithm based on the Dempster-Shafer (D-S) evidence theory is used before filtering. This work presents a new approach to automatically determine mass functions for the D-S evidence theory using the feature information provided by the filter window. Decision rules can determine whether noise exists based on the noise-corrupted belief value. The impulse detection and the noise filtering procedures are progressively applied through several iterations. Finally, the input pixels are identified as either noise-free or noise-corrupted, and only the noise-corrupted pixels in corrupted images are replaced by the mean value of the noise-free pixels in the filter window. Extensive simulation results have demonstrated that the proposed algorithm significantly outperforms other median-based filters.