Dictionary learning (also known as sparse coding) is a representation learning technique that tries to find a sparse representation of the input data as a (sparse) linear combination of basis elements (known as atoms) that construct an over-complete spanning set (known as a dictionary). The mammalian primary visual cortex also works in the same way by exploiting the redundancy and flexibility of this type of representation. In image processing, dictionary learning has been applied on the image patches and it has shown promising results in different image processing problems such as image inpainting, image completion, and denoising. In this recipe, you will learn how to use dictionary learning for image denoising.
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