Anisotropic representations for superresolution of hyperspectral data

We develop a method for superresolution based on anisotropic harmonic analysis. Our ambition is to efficiently increase the resolution of an image without blurring or introducing artifacts, and without integrating additional information, such as sub-pixel shifts of the same image at lower resolutions or multimodal images of the same scene. The approach developed in this article is based on analysis of the directional features present in the image that is to be superesolved. The harmonic analytic technique of shearlets is implemented in order to efficiently capture the directional information present in the image, which is then used to provide smooth, accurate images at higher resolutions. Our algorithm is compared to both a recent anisotropic technique based on frame theory and circulant matrices,1 as well as to the standard superresolution method of bicubic interpolation. We evaluate our algorithm on synthetic test images, as well as a hyperspectral image. Our results indicate the superior performance of anisotropic methods, when compared to standard bicubic interpolation.