Adaptive Partition-Cluster-Based Median Filter for Random-Valued Impulse Noise Removal

As the most popular nonlinear denoise technique, the median filter has attracted significant attention in recent years. In this paper, a novel adaptive median filter is presented to remove random-valued impulse noise in images, named Adaptive Partition-Cluster-Based Median (APCM) Filter. Based on the partition cluster idea, the noise detector classifies pixels into different groups and identifies the noisy pixels in different regions adaptively without iterations. According to the results of noise detection, an improved adaptive decision-based filter is presented to restore the pixels which are corrupted by random-valued impulse noise. The proposed filter technique is open to any impulse noise. Extensive simulation results demonstrate that the proposed method substantially outperforms other state-of-the-arts impulse noise filter techniques both visually and in terms of objective quality measures. Furthermore, the proposed method is much friendly to the hardware parallel implementation of image processing because of its low computation complexity and simple realizable structure.

Download Full-text

Decision Based Median Filter algorithm using Resource Optimized FPGA to Extract Impulse Noise

International Journal of Reconfigurable and Embedded Systems (IJRES) ◽

10.11591/ijres.v3.i1.pp1-10 ◽

2014 ◽

Vol 3 (1) ◽

pp. 1

Author(s):

Rutuja Nandkumar Kulkarni ◽

Pradip C Bhaskar

Keyword(s):

Impulse Noise ◽

Median Filter ◽

Noise Removal ◽

Mean Value ◽

Linear Filter ◽

Noise Detection ◽

Object Boundary ◽

Typical Application ◽

Impulse Noise Removal ◽

Noisy Pixel

Median filter is a non-linear filter used in image processing for impulse noise removal. It finds its typical application in the situations where edges are to be preserved for higher level operations like segmentation, object recognition etc. This paper presents an accurate and efficient noise detection and filtering algorithm for impulse noise removal. The algorithm includes two stages: noise detection followed by noise filtering. The proposed algorithm replaces the noisy pixel by using median value when other pixel values, 0’s or 255’s are present in the selected window and when all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. Similarly algorithm checks for five different conditions to preserve image details, object boundary in high level of noise densities. This median filter was designed, simulated and synthesized on the Xilinx family of FPGAs (XC3S500E of Spartan-3E). The VHDL was used to design the above 2-D median filter using ISE (Xilinx) tool & tested & compared for different grayscale images.

Download Full-text

Impulse Noise Detection and Removal Method Based on Modified Weighted Median

International Journal of Software Innovation ◽

10.4018/ijsi.2020040103 ◽

2020 ◽

Vol 8 (2) ◽

pp. 38-53

Author(s):

Ashpreet ◽

Mantosh Biswas

Keyword(s):

Impulse Noise ◽

Noise Suppression ◽

Extreme Values ◽

Median Filter ◽

Noise Removal ◽

Filter Method ◽

Noise Detection ◽

Median Filters ◽

Removal Method ◽

Weighted Median

Impulse noise generally occurs because of bit errors in progression of image acquisition and transmission. It is well known that median filtering method is an impulse noise removal method. Lots of modified median filters have been proposed in the last decades to improve the methods for noise suppression and detail preservation, which have their own deficiencies while identifying and restoring noise pixels. In this article, after deeply analyzing the reasons, such as decreased noise detection and noise removal accuracy that forms the basis of the deficiencies, this article proposes a modified weighted median filter method for color images corrupted by salt-and-pepper noise. In this method, a pixel is classified into either “noise free pixel” or “noise pixel” by checking the center pixel in the current filtering window with the extreme values (0 or 255) for an 8-bit image using noise detection step. Directional differences and the number of “good” pixels in the current filtering window modify the detected noise pixels. Simulation effects on considered test images reveal the proposed method to be improved over state-of-the-art de-noising methods in terms of PSNR and SSIM with pictorial comparative analysis.

Download Full-text