Abstract
This work addresses the problem of signal-dependent noise removal in images. An adaptive nonlinear filtering approach in the orthogonal transform domain is proposed and analyzed for several typical noise environments in the DCT domain. Being applied locally, that is, within a window of small support, DCT is expected to approximate the Karhunen-Loeve decorrelating transform, which enables effective suppression of noise components. The detail preservation ability of the filter allowing not to destroy any useful content in images is especially emphasized and considered. A local adaptive DCT filtering for the two cases, when signal-dependent noise can be and cannot be mapped into additive uncorrelated noise with homomorphic transform, is formulated. Although the main issue is signal-dependent and pure multiplicative noise, the proposed filtering approach is also found to be competing with the state-of-the-art methods on pure additive noise corrupted images.
Highlights
Digital images are often degraded by noise, due to the imperfection of the acquisition system or the conditions during the acquisition
Till a great number of different image filtering techniques have been designed including nonlinear nonadaptive and adaptive filters [3, 4], transform-based methods [5,6,7,8,9,10,11], techniques based on independent component analysis (ICA), and principal component analysis (PCA) [12, 13], and so forth
We address the case both when multiplicative noise can be and cannot be turned into additive noise with homomorphic transform
Summary
Digital images are often degraded by noise, due to the imperfection of the acquisition system or the conditions during the acquisition. Lee or Kuan filters [23, 24] are among those conventional and widely used techniques that aim to suppress multiplicative noise without the use of the homomorphic transform. The performance of such filters is improved by their integration into an iterative approach [25, 26]. We aim to develop a class of transformbased adaptive filters capable of suppressing signaldependent and multiplicative noise, while preserving texture, edges, and details, which contain significant information for further processing and interpreting of images. For signal-dependent and multiplicative noise, we treat two cases separately: where the homomorphic transform can be and cannot be applied
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