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.

No abstract

No abstract

In many image-processing applications, observed images are contaminated by a nonstationary noise and no a priori information on noise dependence on local mean or about local properties of noise statistics is available. In order to remove such a noise, a locally adaptive filter has to be applied. We study a locally adaptive filter based on evaluation of image local activity in a "blind" manner and on discrete cosine transform computed in overlapping blocks. Two mechanisms of local adaptation are proposed and applied. The first mechanism takes into account local estimates of noise standard deviation while the second one exploits discrimination of homogeneous and heterogeneous image regions by adaptive threshold setting. The designed filter performance is tested for simulated data as well as for real-life remote-sensing and maritime radar images. Recommendations concerning filter parameter setting are provided. An area of applicability of the proposed filter is defined.

International audienceEstimation of noise characteristics is used in various image processing tasks such as edge detection, filtering, reconstruction, compression and segmentation, etc. It is very desirable to have as accurate as possible estimated noise characteristics which influence the quality of further processing. This paper deals with evaluation of accuracy of earlier proposed methods for blind estimation of speckle characteristics. Evaluation is done for TerraSAR-X single-look amplitude images. It is shown that the obtained estimates depend upon image complexity. Besides, parameters of any estimation method influence accuracy (bias) as well. Finally, spatial correlation of noise is yet another factor affecting the obtained estimates. As it is demonstrated, blind estimation in aggregate allows to obtain the estimates of speckle variance with relative error up to 20%, which is appropriate for practical needs. Besides, if speckle variance is estimated, it becomes possible to get accurate estimates of noise spatial correlation in DCT domain. Such estimates can be used in e. g. DCT-based filtering of SAR images