The three main tools in the single image restoration theory are the maximum likelihood (ML) estimator, the maximum a posteriori probability (MAP) estimator, and the set theoretic approach using projection onto convex sets (POCS). This paper utilizes the above known tools to propose a unified methodology toward the more complicated problem of superresolution restoration. In the superresolution restoration problem, an improved resolution image is restored from several geometrically warped, blurred, noisy and downsampled measured images. The superresolution restoration problem is modeled and analyzed from the ML, the MAP, and POCS points of view, yielding a generalization of the known superresolution restoration methods. The proposed restoration approach is general but assumes explicit knowledge of the linear space- and time-variant blur, the (additive Gaussian) noise, the different measured resolutions, and the (smooth) motion characteristics. A hybrid method combining the simplicity of the ML and the incorporation of nonellipsoid constraints is presented, giving improved restoration performance, compared with the ML and the POCS approaches. The hybrid method is shown to converge to the unique optimal solution of a new definition of the optimization problem. Superresolution restoration from motionless measurements is also discussed. Simulations demonstrate the power of the proposed methodology.

Abstract — Printing from an NTSC source and conversion of NTSC source material to high-definition television (HDTV) format are some of the recent applications that motivate superresolution (SR) image and video reconstruction from lowresolution (LR) and possibly blurred sources. Existing methods for SR image reconstruction are limited by the assumptions that the input LR images are sampled progressively, and that the aperture time of the camera is zero, thus ignoring the motion blur occurring during the aperture time. Because of the observed adverse effects of these assumptions for many common video sources, this paper proposes i) a complete model of video acquisition with an arbitrary input sampling lattice and a nonzero aperture time, and ii) an algorithm based on this model using the theory of projections onto convex sets to reconstruct SR still images or video from an LR time sequence of images. Experimental results with real video are provided, which clearly demonstrate that a significant increase in the image resolution can be achieved by taking the motion blurring into account especially when there exists large interframe motion. Index Terms — Superresolution, video stills, video resampling, standards conversion. I.

Recent years have seen growing interest in the problem of super-resolution restoration of video sequences. Whereas in the traditional single image restoration problem only a single input image is available for processing, the task of reconstructing super-resolution images from multiple undersampled and degraded images can take advantage of the additional spatiotemporal data available in the image sequence. In particular, camera and scene motion lead to frames in the source video sequence containing similar, but not identical information. The additional information available in these frames make possible reconstruction of visually superior frames at higher resolution than that of the original data. In this paper we review the current state of the art and identify promising directions for future research.

Abstract—Hyperspectral images are used for aerial and space imagery applications, including target detection, tracking, agricultural, and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles, and sensor noise degrade the quality of these images. Improving their resolution has a high payoff, but applying superresolution techniques separately to every spectral band is problematic for two main reasons. First, the number of spectral bands can be in the hundreds, which increases the computational load excessively. Second, considering the bands separately does not make use of the information that is present across them. Furthermore, separate band super resolution does not make use of the inherent low dimensionality of the spectral data, which can effectively be used to improve the robustness against noise. In this paper, we introduce a novel super-resolution method for hyperspectral images. An integral part of our work is to model the hyperspectral image acquisition process. We propose a model that enables us to represent the hyperspectral observations from different wavelengths as weighted linear combinations of a small number of basis image planes. Then, a method for applying super resolution to hyperspectral images using this model is presented. The method fuses information from multiple observations and spectral bands to improve spatial resolution and reconstruct the spectrum of the observed scene as a combination of a small number of spectral basis functions. Index Terms—Hyperspectral, image reconstruction, information fusion, resolution enhancement, spectral, super resolution.

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Improving the spatial resolution of an image sequence critically depends upon the accuracy of the motion estimator. The problem is complicated by the fact that the motion field is prone to significant errors since original high resolution images are not available. In earlier work, bilinearly interpolated images were used as initial conditions for the proposed algorithm. In this paper, the use of other initial conditions, such as previously estimated images, is experimentally investigated. Furthermore, various forms of the iterative video resolution enhancement algorithm are studied experimentally.

We derive a novel method to determine the parameters for regularized super-resolution problems, addressing both the traditional regularized super-resolution problem with single- and multiple-parameters and the simultaneous super-resolution problem with two parameters. The proposal relies on the Joint Maximum a Posteriori (JMAP) estimation technique. The classical JMAP technique provides solutions at low computational cost, but it may be unstable and presents multiple local minima. We propose to stabilize the JMAP estimation, while achieving a cost function with an unique global solution, by assuming a gamma prior distribution for the hyperparameters. The resulting fidelity is similar to the quality provided by classical methods such as GCV, L-curve and Evidence, which are computationally expensive. Experimental results illustrate the low complexity and stability of the proposed method.

In this paper, an approach to improve the spatial resolution of color video is presented. Such high resolution images are desired, for example, in video printing. Previous work has shown that the most important step in achieving high quality results is the accuracy of the motion field. It is well known that motion estimation is an ill-posed problem. However, in processing color video, additional information contained in the color channels may be used to improve the accuracy of the motion field over the motion field obtained with the use of only one channel. In turn, this improvement in the motion field will be shown through several experimental results to significantly improve the estimation of a high resolution image sequence from a corresponding observed low resolution sequence.

Space-adaptive regularization (SAR) is a method for image restoration and it can decrease ring artifacts arising around sharp intensity transition of the image. The approach proposed in this paper incorporates incremental wiener filter (IWF) with SAR by edge-picking method and then estimates a single high-resolution image from different low-resolution images. The characters of spatial piecewise smoothness in SAR and mpid convergence in incremental wiener filter are combined in the new approach. The computer simulations show that this method has good convergence performance. 1.

This paper proposes a new approach to subpixel registration, under local/global shifts or rotation, using the phase-difference matrix. We establish the exact relationship between the continuous and the discrete phase difference of two shifted images and show that their discrete phase difference is a 2-dimensional sawtooth signal. As a result, the exact shifts or rotations can be determined to subpixel or subangle accuracy by counting the number of cycles of the phase-difference matrix along the frequency axes. The subpixel portion is represented by a fraction of a cycle corresponding to the noninteger part of the shift or rotation. The rotation angle is estimated by applying our method using a polar coordinate system. The problem is formulated as an overdetermined system of equations and is solved by imposing a regularity constraint. The tradeoff for imposing the constraint is determined by exploiting the rank constraint leading to a closed-form expression for the optimal regularization parameter.