Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks

Convolutional Neural Networks (CNNs) have been consistently proved state-of-the-art results in image Super-Resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data. However, most of the works published in the literature have been focusing on the Single-Image Super-Resolution problem so far. At present, satellite based remote sensing platforms offer huge data availability with high temporal resolution and low spatial resolution. In this context, the presented research proposes a novel residual attention model (RAMS) that efficiently tackles the multi-image super-resolution task, simultaneously exploiting spatial and temporal correlations to combine multiple images. We introduce the mechanism of visual feature attention with 3D convolutions in order to obtain an aware data fusion and information extraction of the multiple low-resolution images, transcending limitations of the local region of convolutional operations. Moreover, having multiple inputs with the same scene, our representation learning network makes extensive use of nestled residual connections to let flow redundant low-frequency signals and focus the computation on more important high-frequency components. Extensive experimentation and evaluations against other available solutions, either for single or multi-image super-resolution, have demonstrated that the proposed deep learning-based solution can be considered state-of-the-art for Multi-Image Super-Resolution for remote sensing applications

Face image super-resolution using 2D CCA

In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V

Characterizing Magnetohydrodynamic Turbulence in the Small Magellanic Cloud

We investigate the nature and spatial variations of turbulence in the Small Magellanic Cloud (SMC) by applying several statistical methods on the neutral hydrogen (HI) column density image of the SMC and a database of isothermal numerical simulations. By using the 3rd and 4th statistical moments we derive the spatial distribution of the sonic Mach number (M_s) across the SMC. We find that about 90% of the HI in the SMC is subsonic or transonic. However, edges of the SMC `bar' have M_s=4 and may be tracing shearing or turbulent flows. Using numerical simulations we also investigate how the slope of the spatial power spectrum depends on both sonic and Alfven Mach numbers. This allows us to gauge the Alfven Mach number of the SMC and conclude that its gas pressure dominates over the magnetic pressure. The super-Alfvenic nature of the HI gas in the SMC is also highlighted by the bispectrum, a three-point correlation function which characterizes the level of non-Gaussianity in wave modes. We find that the bispectrum of the SMC HI column density displays similar large-scale correlations as numerical simulations, however it has localized enhancements of correlations. In addition, we find a break in correlations at a scale of 160 pc. This may be caused by numerous expanding shells of a similar size

Limits of multi-frame image enhancement: a case of super-resolution

A common and important problem that arises in visual communications is the need to create an enhanced-resolution video image sequence from a lower resolution input video stream. This can be accomplished by exploiting the spatial correlations that exist between successive video frames using Super-Resolution (SR) reconstruction. SR refers to the task of increasing the spatial resolution through multiple frame processing. Multi-frame resolution enhancement methods are of increasing interest in digital image processing and there has been a substantial amount of research in developing algorithms that combine a set of low-quality images to produce a set of higher quality images. Either explicitly or implicitly, such algorithms must perform the common task of registering and fusing the lowquality image data. While many such processes have been proposed, very little work has addressed their limits. In this context, an algorithm designed to operate in the spatial domain is used in a controlled test to compute a higher-resolution image by mapping a model of the image formation process using local sub-pixel shifts among the lower resolution and compressed images of the same scene. These shifts are determined by way of a rigorous least-squares area-based image-matching scheme that does not require control points. Statistical results show that the performance of the algorithm does degrade, as would be expected, depending on (1) the amount of noise present in the low-resolution images, (2) the number of low-resolution input images and (3) the magnification factor required to meet resolution requirements