  Sparse representation of real-life images is a very effective approach in
imaging applications, such as denoising. In recent years, with the growth of
computing power, data-driven strategies exploiting the redundancy within
patches extracted from one or several images to increase sparsity have become
more prominent. This paper presents a novel image denoising algorithm
exploiting such an image-dependent basis inspired by the quantum many-body
theory. Based on patch analysis, the similarity measures in a local image
neighborhood are formalized through a term akin to interaction in quantum
mechanics that can efficiently preserve the local structures of real images.
The versatile nature of this adaptive basis extends the scope of its
application to image-independent or image-dependent noise scenarios without any
adjustment. We carry out a rigorous comparison with contemporary methods to
demonstrate the denoising capability of the proposed algorithm regardless of
the image characteristics, noise statistics and intensity. We illustrate the
properties of the hyperparameters and their respective effects on the denoising
performance, together with automated rules of selecting their values close to
the optimal one in experimental setups with ground truth not available.
Finally, we show the ability of our approach to deal with practical images
denoising problems such as medical ultrasound image despeckling applications.
