Performance Evaluation of Noise Reduction Filters for Color Images through Normalized Color Difference (NCD) Decomposition

Fabrizio Russo

doi:10.1155/2014/579658

Abstract

Removing noise without producing image distortion is the challenging goal for any image denoising filter. Thus, the different amounts of residual noise and unwanted blur should be evaluated to analyze the actual performance of a denoising process. In this paper a novel full-reference method for measuring such features in color images is presented. The proposed approach is based on the decomposition of the normalized color difference (NCD) into three components that separately take into account different classes of filtering errors such as the inaccuracy in filtering noise pulses, the inaccuracy in reducing Gaussian noise, and the amount of collateral distortion. Computer simulations show that the proposed method offers significant advantages over other measures of filtering performance in the literature, including the recently proposed vector techniques.

Highlights

It is known that removal of noise and preservation of color/ structural information are very difficult and challenging issues in the design of image denoising filters [1]
Performance evaluation of noise reduction techniques needs appropriate full-reference metrics able to measure the different amounts of residual noise and filtering distortion
In this paper we have presented a new method for evaluating such features in color images restored from impulse and Gaussian noise

Summary

Introduction

It is known that removal of noise and preservation of color/ structural information are very difficult and challenging issues in the design of image denoising filters [1]. They cannot distinguish between noise cancellation and detail preservation yielded by a filter because different combi nations of image blur and unfiltered noise can lead to the same score Proposed measures such as the vector root mean squared error (VRMSE) are a more appropriate choice because they give a separate evaluation of the mentioned features. A limitation of such techniques, is the fact that they work in the RGB [24] and YUV [25] nonuniform color spaces and measure the noise removal and the detail blur in the luminance component of the image only They cannot address the case of mixed (impulse and Gaussian) noise in color data.

The Proposed Method

How the Method Works

Results of Computer Simulations

Conclusions