Abstract
This article addresses under which conditions filtering can visibly improve the image quality. The key points are the following. First, we analyze filtering efficiency for 25 test images, from the color image database TID2008. This database allows assessing filter efficiency for images corrupted by different noise types for several levels of noise variance. Second, the limit of filtering efficiency is determined for independent and identically distributed (i.i.d.) additive noise and compared to the output mean square error of state-of-the-art filters. Third, component-wise and vector denoising is studied, where the latter approach is demonstrated to be more efficient. Fourth, using of modern visual quality metrics, we determine that for which levels of i.i.d. and spatially correlated noise the noise in original images or residual noise and distortions because of filtering in output images are practically invisible. We also demonstrate that it is possible to roughly estimate whether or not the visual quality can clearly be improved by filtering.
Highlights
A huge amount of color images is acquired nowadays by professional and consumer digital cameras, mobile phones, remote sensing systems, etc., and used for various purposes [1,2,3,4,5]
We have considered several values of additive white (i.i.d.) Gaussian noise (AWGN) noise variance smaller than 65
The images were corrupted by AWGN and spatially correlated noise with a wide set of variance values
Summary
A huge amount of color images is acquired nowadays by professional and consumer digital cameras, mobile phones, remote sensing systems, etc., and used for various purposes [1,2,3,4,5]. 2. For original (noisy) images, the lowest visual quality takes place for the simplest structure images (the smallest values of PSNR-HVS-Mk (n) are observed for the test images ##2, 3, 4, 15, 16, 20, and 23, about 33 dB for all of them); this deals with the fact that for textural images noise is considerably masked while for simple structure images it is well seen in homogeneous image regions. If more sophisticated filtering methods than componentwise DCT-based denoising are applied, it is possible to provide almost “ideal” visual quality of processed images (PSNR-HVS-Mk > 40 dB) for values of noise variance larger than 25. As water surface noise is practically not seen because of masking effects These observations are confirmed by the map of s2HVS-M lm for noisy (original) image presented in Figure 5b (further denoted as s2HVSM or lm, brighter pixels correspond to blocks with larger s2HVS-M or lm). Simpler structure, test image #3 corrupted by AWGN with larger noise variance equal to
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