An Efficient Universal Noise Removal Algorithm Combining Spatial Gradient and Impulse Statistic

Shuhan Chen,Wenjie Zhang,Weiren Shi

doi:10.1155/2013/480274

Abstract

We propose a novel universal noise removal algorithm by combining spatial gradient and a new impulse statistic into the trilateral filter. By introducing a reference image, an impulse statistic is proposed, which is called directional absolute relative differences (DARD) statistic. Operation was carried out in two stages: getting reference image and image denoising. For denoising, we introduce the spatial gradient into the Gaussian filtering framework for Gaussian noise removal and integrate our DARD statistic for impulse noise removal, and finally we combine them together to create a new trilateral filter for mixed noise removal. Simulation results show that our noise detector has a high classification rate, especially for salt-and-pepper noise. And the proposed approach achieves great results both in terms of quantitative measures of signal restoration and qualitative judgments of image quality. In addition, the computational complexity of the proposed method is less than that of many other mixed noise filters.

Highlights

Noise can be introduced into digital images due to analog-to-digital conversion errors and malfunctioning pixel elements in the camera sensors [1, 2]
Introduces a reference image which is obtained by standard median filter to solve this problem; the edge direction information is still not considered
The DWM filter introduces the edge direction information into the median filter, the performance hardly dependeds on the accuracy of edge direction calculation

Summary

Introduction

Noise can be introduced into digital images due to analog-to-digital conversion errors and malfunctioning pixel elements in the camera sensors [1, 2]. Based on ROAD, Dong et al proposed a rank-ordered logarithmic differences (ROLD) statistic to improve the accuracy of noise detection [21] It obtained better performance, its running time is significantly increased comparing with the previous mentioned filters due to the logarithmic computation. In [22], Yu et al presented a rank-ordered relative differences (RORD) statistic through introducing a reference image and combining with a simple weighted mean filter It can remove impulse noise and preserve image details. The median-based signal-dependent rank-ordered mean (SD-ROM) filter proposed by Abreu et al can be used for mixed impulse and Gaussian noise removal [23] It often produces visually disappointing output when applied to images with Gaussian or mixed noise [2].

DARD Statistic for Detecting Impulse Noise

The Proposed Method

Simulations

Method

Findings

Conclusion