Benchmarking of Multispectral Pansharpening: Reproducibility, Assessment, and Meta-Analysis.

The term pansharpening denotes the process by which the geometric resolution of a multiband image is increased by means of a co-registered broadband panchromatic observation of the same scene having greater spatial resolution. Over time, the benchmarking of pansharpening methods has revealed itself to be more challenging than the development of new methods. Their recent proliferation in the literature is mostly due to the lack of a standardized assessment.

Multimodal fusion of tomographic sequences of medical images: MRE spatially enhanced by MRI.

Background And Objective: In biomedical fields, image analysis is often necessary for an accurate diagnosis. In order to obtain all the information needed to form an in-depth clinical picture, it may be useful to combine the contents of images taken under different diagnostic modes. Multimodal medical image fusion techniques enable complementary information acquired by different imaging devices to be automatically combined into a unique image.

Volumetric interpolation of tomographic sequences for accurate 3D reconstruction of anatomical parts.

Background And Objective: Tomographic sequences of biomedical images are commonly used to achieve a three-dimensional visualization of the human anatomy. In some cases, the number of images contained in the sequence is limited, e.g.

Comments on "A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions".

In this paper, discrepancies and reference inaccuracies in the paper (Grana et al., 2003) are pointed out. Specifically, it is demonstrated that the definitions of "lesion gradient" and "skin lesion gradient," widely used in a number of medical papers on computer analysis of pigmented skin lesions, are unambiguous, and that the "new algorithm for border description" described in the subject paper substantially relies on well-established concepts dating back over one decade ago.

Multiresolution MAP despeckling of SAR images based on locally adaptive generalized Gaussian pdf modeling.

In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori MIAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame.