An Improved Observation Model for Super-Resolution under Affine Motion

Super-resolution (SR) techniques make use of subpixel shifts between frames in an image sequence to yield higher-resolution images. We propose an original observation model devoted to the case of non isometric inter-frame motion as required, for instance, in the context of airborne imaging sensors. First, we describe how the main observation models used in the SR literature deal with motion, and we explain why they are not suited for non isometric motion. Then, we propose an extension of the observation model by Elad and Feuer adapted to affine motion. This model is based on a decomposition of affine transforms into successive shear transforms, each one efficiently implemented by row-by-row or column-by-column 1-D affine transforms. We demonstrate on synthetic and real sequences that our observation model incorporated in a SR reconstruction technique leads to better results in the case of variable scale motions and it provides equivalent results in the case of isometric motions

Super Resolution of Wavelet-Encoded Images and Videos

In this dissertation, we address the multiframe super resolution reconstruction problem for wavelet-encoded images and videos. The goal of multiframe super resolution is to obtain one or more high resolution images by fusing a sequence of degraded or aliased low resolution images of the same scene. Since the low resolution images may be unaligned, a registration step is required before super resolution reconstruction. Therefore, we first explore in-band (i.e. in the wavelet-domain) image registration; then, investigate super resolution. Our motivation for analyzing the image registration and super resolution problems in the wavelet domain is the growing trend in wavelet-encoded imaging, and wavelet-encoding for image/video compression. Due to drawbacks of widely used discrete cosine transform in image and video compression, a considerable amount of literature is devoted to wavelet-based methods. However, since wavelets are shift-variant, existing methods cannot utilize wavelet subbands efficiently. In order to overcome this drawback, we establish and explore the direct relationship between the subbands under a translational shift, for image registration and super resolution. We then employ our devised in-band methodology, in a motion compensated video compression framework, to demonstrate the effective usage of wavelet subbands. Super resolution can also be used as a post-processing step in video compression in order to decrease the size of the video files to be compressed, with downsampling added as a pre-processing step. Therefore, we present a video compression scheme that utilizes super resolution to reconstruct the high frequency information lost during downsampling. In addition, super resolution is a crucial post-processing step for satellite imagery, due to the fact that it is hard to update imaging devices after a satellite is launched. Thus, we also demonstrate the usage of our devised methods in enhancing resolution of pansharpened multispectral images

Superresolution imaging: A survey of current techniques

Cristóbal, G., Gil, E., Šroubek, F., Flusser, J., Miravet, C., Rodríguez, F. B., “Superresolution imaging: A survey of current techniques”, Proceedings of SPIE - The International Society for Optical Engineering, 7074, 2008. Copyright 2008. Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.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.This research has been partially supported by the following grants: TEC2007-67025/TCM, TEC2006-28009-E, BFI-2003-07276, TIN-2004-04363-C03-03 by the Spanish Ministry of Science and Innovation, and by PROFIT projects FIT-070000-2003-475 and FIT-330100-2004-91. Also, this work has been partially supported by the Czech Ministry of Education under the project No. 1M0572 (Research Center DAR) and by the Czech Science Foundation under the project No. GACR 102/08/1593 and the CSIC-CAS bilateral project 2006CZ002

Four-dimensional cardiac imaging in living embryos via postacquisition synchronization of nongated slice sequences

Being able to acquire, visualize, and analyze 3D time series (4D data) from living embryos makes it possible to understand complex dynamic movements at early stages of embryonic development. Despite recent technological breakthroughs in 2D dynamic imaging, confocal microscopes remain quite slow at capturing optical sections at successive depths. However, when the studied motion is periodic— such as for a beating heart—a way to circumvent this problem is to acquire, successively, sets of 2D+time slice sequences at increasing depths over at least one time period and later rearrange them to recover a 3D+time sequence. In other imaging modalities at macroscopic scales, external gating signals, e.g., an electro-cardiogram, have been used to achieve proper synchronization. Since gating signals are either unavailable or cumbersome to acquire in microscopic organisms, we have developed a procedure to reconstruct volumes based solely on the information contained in the image sequences. The central part of the algorithm is a least-squares minimization of an objective criterion that depends on the similarity between the data from neighboring depths. Owing to a wavelet-based multiresolution approach, our method is robust to common confocal microscopy artifacts. We validate the procedure on both simulated data and in vivo measurements from living zebrafish embryos

Recent Advances in Registration, Integration and Fusion of Remotely Sensed Data: Redundant Representations and Frames

In recent years, sophisticated mathematical techniques have been successfully applied to the field of remote sensing to produce significant advances in applications such as registration, integration and fusion of remotely sensed data. Registration, integration and fusion of multiple source imagery are the most important issues when dealing with Earth Science remote sensing data where information from multiple sensors, exhibiting various resolutions, must be integrated. Issues ranging from different sensor geometries, different spectral responses, differing illumination conditions, different seasons, and various amounts of noise need to be dealt with when designing an image registration, integration or fusion method. This tutorial will first define the problems and challenges associated with these applications and then will review some mathematical techniques that have been successfully utilized to solve them. In particular, we will cover topics on geometric multiscale representations, redundant representations and fusion frames, graph operators, diffusion wavelets, as well as spatial-spectral and operator-based data fusion. All the algorithms will be illustrated using remotely sensed data, with an emphasis on current and operational instruments

Feature Extraction for image super-resolution using finite rate of innovation principles

To understand a real-world scene from several multiview pictures, it is necessary to find the disparities existing between each pair of images so that they are correctly related to one another. This process, called image registration, requires the extraction of some specific information about the scene. This is achieved by taking features out of the acquired images. Thus, the quality of the registration depends largely on the accuracy of the extracted features. Feature extraction can be formulated as a sampling problem for which perfect re- construction of the desired features is wanted. The recent sampling theory for signals with finite rate of innovation (FRI) and the B-spline theory offer an appropriate new frame- work for the extraction of features in real images. This thesis first focuses on extending the sampling theory for FRI signals to a multichannel case and then presents exact sampling results for two different types of image features used for registration: moments and edges. In the first part, it is shown that the geometric moments of an observed scene can be retrieved exactly from sampled images and used as global features for registration. The second part describes how edges can also be retrieved perfectly from sampled images for registration purposes. The proposed feature extraction schemes therefore allow in theory the exact registration of images. Indeed, various simulations show that the proposed extraction/registration methods overcome traditional ones, especially at low-resolution. These characteristics make such feature extraction techniques very appropriate for applications like image super-resolution for which a very precise registration is needed. The quality of the super-resolved images obtained using the proposed feature extraction meth- ods is improved by comparison with other approaches. Finally, the notion of polyphase components is used to adapt the image acquisition model to the characteristics of real digital cameras in order to run super-resolution experiments on real images

Superresolution Enhancement of Hyperspectral CHRIS/Proba Images With a Thin-Plate Spline Nonrigid Transform Model

Given the hyperspectral-oriented waveband configuration of multiangular CHRIS/Proba imagery, the scope of its application could widen if the present 18-m resolution would be improved. The multiangular images of CHRIS could be used as input for superresolution (SR) image reconstruction. A critical procedure in SR is an accurate registration of the low-resolution images. Conventional methods based on affine transformation may not be effective given the local geometric distortion in high off-nadir angular images. This paper examines the use of a non-rigid transform to improve the result of a nonuniform interpolation and deconvolution SR method. A scale-invariant feature transform is used to collect control points (CPs). To ensure the quality of CPs, a rigorous screening procedure is designed: 1) an ambiguity test; 2) the m-estimator sample consensus method; and 3) an iterative method using statistical characteristics of the distribution of random errors. A thin-plate spline (TPS) nonrigid transform is then used for the registration. The proposed registration method is examined with a Delaunay triangulation-based nonuniform interpolation and reconstruction SR method. Our results show that the TPS nonrigid transform allows accurate registration of angular images. SR results obtained from simulated LR images are evaluated using three quantitative measures, namely, relative mean-square error, structural similarity, and edge stability. Compared to the SR methods that use an affine transform, our proposed method performs better with all three evaluation measures. With a higher level of spatial detail, SR-enhanced CHRIS images might be more effective than the original data in various applications.JRC.H.7-Climate Risk Managemen