This paper introduces a structure from motion (SfM)-based surface reconstruction method for images including weak textures and structures. In SfM, the quality of the determination of homologous points between images plays a key role in terms of reconstruction performances. However, classical feature matching-based methods as integrated in the state-of-the-art SfM-algorithms are often inoperative for images with weak structures and textures. This contribution describes a dense optical flow-based solution enabling the point correspondence determination in such scenes. The accuracy and robustness of the proposed method were validated using results obtained for a phantom with known dimensions and with real medical data, respectively. Complex internal stomach wall surfaces were constructed using gastroscopic images.

This paper introduces a robust image mosaicing method. A variational optical flow (OF) method is first proposed to deal with scenes exhibiting strong specular reflections and few texture information. Then, a general form of descriptors invariant to complex illumination variations is given from which a novel descriptor is obtained. Non-linear transformations computed with the OF fields between the images are used to construct the mosaics. Experimental results demonstrate that the proposed method leads to coherent mosaics, even for complex gastroscopic image sequences. Furthermore, it can be potentially used to build mosaics for non-medical scenes.

This paper introduces a generalized descriptor formulation facilitating the design of illumination invariant data-terms in variational optical flow. This contribution also proposes a criterion to check whether a patch-based descriptor is illumination invariant or not. To do so, a local model is used to simulate complex illumination changes between images. As an example, it is shown how a novel patch-based descriptor can be derived from the generalized form. The performances of this descriptor are compared to those of reference descriptors in the literature using data sets with and without strong illumination changes. The accuracy and robustness of the proposed descriptor is also demonstrated through tests on gastroscopic image sequences including complex illumination changes.

Denoising is an essential application to improve image quality, especially in medical imaging. This paper introduces an example and patch-based learning method for reducing Gaussian noise and Poisson noise which often appear in medical imaging modalities using ionizing radiation. In the proposed method, denoising is performed by learning the regression model based on a set of the nearest neighbors of a given noisy patch, with the help of a given set of standard images. The method is evaluated and compared to several state-of-the-art denoising methods. The obtained results confirm its efficiency, especially for heavy noise.

In this paper, we propose a novel example-based method for denoising and super-resolution of medical images. The objective is to estimate a high-resolution image from a single noisy low-resolution image, with the help of a given database of high and low-resolution image patch pairs. Denoising and super-resolution in this paper is performed on each image patch. For each given input low-resolution patch, its high-resolution version is estimated based on finding a nonnegative sparse linear representation of the input patch over the low-resolution patches from the database, where the coefficients of the representation strongly depend on the similarity between the input patch and the sample patches in the database. The problem of finding the nonnegative sparse linear representation is modeled as a nonnegative quadratic programming problem. The proposed method is especially useful for the case of noise-corrupted and low-resolution image. Experimental results show that the proposed method outperforms other state-of-the-art super-resolution methods while effectively removing noise.

Medical images are often corrupted by random noise, leading to undesirable visual quality. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. In this paper, we propose a novel learning method for the reduction of Gaussian noise of Computed Tomography (CT) image and Rician noise of Magnetic Resonance Imaging (MRI) image based on a given set of standard images and the Kernel Ridge Regression (KRR). Experimental results demonstrate the outperformance of the proposed technique over various other methods in terms of both objective and subjective evaluations.