Abstract / truncated to 115 words (read the full abstract)

When an image is acquired by a digital imaging sensor, it is always degraded by some noise. This leads to two basic questions: What are the main characteristics of this noise? How to remove it? These questions in turn correspond to two key problems in signal processing: noise estimation and noise removal (so-called denoising). This thesis addresses both abovementioned problems and provides a number of original and effective contributions for their solution. The first part of the thesis introduces a novel image denoising algorithm based on the low-complexity Shape-Adaptive Discrete Cosine Transform (SA-DCT). By using spatially adaptive supports for the transform, the quality of the filtered image is high, with clean edges and without disturbing ... toggle 8 keywords

denoising – noise estimation – shape-adaptive – DCT – discrete cosine – anisotropic – raw data – noise modelling

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