Medical images often suffer from noise and low-resolution, which may compromise the accuracy of diagnosis. How to improve the image resolution in cases of heavy noise is still a challenging issue. This paper introduces a novel Examplebased Super-resolution (SR) method for medical images corrupted by heavy Poisson noise, integrating efficiently denoising and SR in the same framework. The purpose is to estimate a high-resolution (HR) image from a single noisy low-resolution (LR) image, with the help of a given set of standard images which are used as examples to construct the database. Precisely, for each patch in the noisy LR image, the idea is to find its nearest neighbor patches from the database and use them to estimate the HR patch by computing a regression function based on the construction of a reproducing kernel Hilbert space. To obtain the corresponding set of k-nearest neighbors in the database, a coarse search using the shortest Euclidean distance is first performed, followed by a refined search using a criterion based on the distribution of Poisson noise and the Anscombe transformation. This paper also evaluates the performance of the method comparing to other state-of-the-art denoising methods and SR methods. The obtained results demonstrate its efficiency, especially for heavy Poisson noise.
Kernel-design based method such as Bilateral filter (BIL), non-local means (NLM) filter is known as one of the
most attractive approaches for denoising. We propose in this paper a new noise filtering method inspired by BIL,
NLM filters and principal component analysis (PCA). The main idea here is to perform the BIL in a multidimensional
PCA-space using an anisotropic kernel. The filtered multidimensional signal is then transformed back
onto the image spatial domain to yield the desired enhanced image. In this work, it is demonstrated that the
proposed method is a generalization of kernel-design based methods. The obtained results are highly promising.