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. 1. 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]. Indeed, the quality of a filtered image is typically impaired by the superposition of two different effects: insufficient noise cancellation and unwanted collateral distortion produced by the filtering. Since the different amounts of these effects should separately be taken into account to analyze the behavior of any image denoising technique, the development of appropriate metrics is of paramount importance. Until recently, the most common methods to evaluate the quality of denoised images were combinations of visual inspection and objective measurements based on the computation of pixelwise differences between the original and the processed image. Typically, the mean squared error (MSE) or the peak signal-to-noise ratio (PSNR) was adopted to measure the noise cancellation, whereas the mean absolute error (MAE) represented the most commonly used metrics to evaluate the edge preservation. All the aforementioned measures are typically evaluated in the RGB coordinate system, that is, the most popular color space for a variety of applications. In order to deal with the human perception of colors (not adequately described by the RGB space), another kind of metrics, namely, the normalized color difference (NCD), was proposed [1–3]. Such measure is evaluated in the perceptually uniform CIE Luv (or CIE Lab) color spaces in order to appraise the perceptual closeness of a filtered picture to the uncorrupted original. The results of most filters in the literature have been evaluated by resorting to the aforementioned measures or
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