  Decomposing an image through Fourier, DCT or wavelet transforms is still a
common approach in digital image processing, in number of applications such as
denoising. In this context, data-driven dictionaries and in particular
exploiting the redundancy withing patches extracted from one or several images
allowed important improvements. This paper proposes an original idea of
constructing such an image-dependent basis inspired by the principles of
quantum many-body physics. The similarity between two image patches is
introduced in the formalism through a term akin to interaction terms in quantum
mechanics. The main contribution of the paper is thus to introduce this
original way of exploiting quantum many-body ideas in image processing, which
opens interesting perspectives in image denoising. The potential of the
proposed adaptive decomposition is illustrated through image denoising in
presence of additive white Gaussian noise, but the method can be used for other
types of noise such as image-dependent noise as well. Finally, the results show
that our method achieves comparable or slightly better results than existing
approaches.
