Image Denoising via 2-D FIR Filtering Approach

2013 ◽  
pp. 184-198
Author(s):  
Jingyu Hua ◽  
Wankun Kuang
Keyword(s):  
Computer Simulation ◽  
Image Denoising ◽  
Bilateral Filtering ◽  
Learning Method ◽  
Swarm Optimization ◽  
Filtering Method ◽  
Fir Filtering ◽  
Mean Filter ◽  
Filtering Approach ◽  
Lowpass Filtering

Image denoising has received much concern for decades. One of the simplest methods for image denoising is the 2-D FIR lowpass filtering approach. Firstly, the authors make a comparative study of the conventional lowpass filtering approach, including the classical mean filter and three 2-D FIR LowPass Filters (LPF) designed by McClellan transform. Then an improved method based on learning method is presented, where pixels are filtered by five edge-oriented filters, respectively, facilitated to their edge details. Differential Evolution Particle Swarm Optimization (DEPSO) algorithm is exploited to refine those filters. Computer simulation demonstrates that the proposed method can be superior to the conventional filtering method, as well as the modern Bilateral Filtering (BF) and the Stochastic Denoising (SD) method.

scholarly journals Design of median-type filters with an impulse noise detector using decision tree and particle swarm optimization for image restoration

2010 ◽  
Vol 7 (4) ◽  
pp. 859-882 ◽  
Author(s):  
Bae-Muu Chang ◽  
Hung-Hsu Tsai ◽  
Xuan-Ping Lin ◽  
Pao-Ta Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
High Performance ◽  
Impulse Noise ◽  
Median Filter ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Ordered Median ◽  
Mean Filter ◽  
Hybrid Detector

This paper proposes the median-type filters with an impulse noise detector using the decision tree and the particle swarm optimization, for the recovery of the corrupted gray-level images by impulse noises. It first utilizes an impulse noise detector to determine whether a pixel is corrupted or not. If yes, the filtering component in this method is triggered to filter it. Otherwise, the pixel is kept unchanged. In this work, the impulse noise detector is an adaptive hybrid detector which is constructed by integrating 10 impulse noise detectors based on the decision tree and the particle swarm optimization. Subsequently, the restoring process in this method respectively utilizes the median filter, the rank ordered mean filter, and the progressive noise-free ordered median filter to restore the corrupted pixel. Experimental results demonstrate that this method achieves high performance for detecting and restoring impulse noises, and outperforms the existing well-known methods.

Sign in / Sign up

Export Citation Format

Share Document