Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may exhibit insufficient spatial and temporal resolution. In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). The stability of these methods depends on having more than one image of the same frame. Differences between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of current SR techniques we describe two recently developed SR methods by the authors. First, we introduce a variational method that minimizes a regularized energy function with respect to the high resolution image and blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods.

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This paper presents the results of measuring the image quality of a video compression system based in the H.264 standard using the Anisotropic Quality Index (AQI). These results have been compared with the quality measured by means of the traditionally used Peak Signal to Noise Ratio (PSNR). The PSNR has demonstrated to be an unreliable way to compute the perceptual quality of images. Although it is widely used because its simplicity and immediacy to be computed, the PSNR and other methods based in the image differences measurement (as the Root Mean Squared Error or RMSE) experience the problem of not properly reflecting the real perceptual image quality. Images with the same amount of noise can present similar PSNRs values even with very different perceptual appearance. In the other side, the AQI has proven to be a more reliable way to analytically measure the perceptual image quality. This new measure is based on the use of a particular type of the high-order Rényi entropies. This method is based on measuring the anisotropy of the image through the variance of the expected value of the pixel-wise directional image entropy. Moreover, the AQI has the additional benefit of not needing a reference image. The reference image, compulsory in the PSNR computation, is usually impossible to obtain in real situations, thus relegating the PSNR only to test-bench developments. The possibility of computing the AQI opens the ability of self-regulated compression systems based on the adjustment of parameters that exhibit greater influence on the final image quality. This work shows the results of compressing several standard video sequences using the H.264 video compression standard. Compared with the PSNR, the AQI represents a better indicator of the perceptual quality of images.