  In this paper, we propose a novel image denoising algorithm exploiting
features from both spatial as well as transformed domain. We implement
intensity-invariance based improved grouping for collaborative support-agnostic
sparse reconstruction. For collaboration firstly, we stack similar-structured
patches via intensity-invariant correlation measure. The grouped patches
collaborate to yield desirable sparse estimates for noise filtering. This is
because similar patches share the same support in the transformed domain, such
similar supports can be used as probabilities of active taps to refine the
sparse estimates. This ultimately produces a very useful patch estimate thus
increasing the quality of recovered image by discarding the noise-causing
components. A region growing based spatially developed post-processor is then
applied to further enhance the smooth regions by extracting the spatial domain
features. We also extend our proposed method for denoising of color images.
Comparison results with the state-of-the-art algorithms in terms of peak
signal-to-noise ratio (PNSR) and structural similarity (SSIM) index from
extensive experimentations via a broad range of scenarios demonstrate the
superiority of our proposed algorithm.
