Super-resolution Restoration Research Articles

Robust super-resolution by minimizing a Gaussian-weighted L2 error norm

Super-resolution restoration is the problem of restoring a high-resolution scene from multiple degraded low-resolution images under motion. Due to imaging blur and noise, this problem is ill-posed. Additional constraints such as smoothness of the solution via regularization is often required to obtain a stable solution. While adding a regularization term to the cost function is a standard practice in image restoration, we propose a restoration algorithm that does not require this extra regularization term. The robustness of the algorithm is achieved by a Gaussian-weighted L2-norm in the data misfit term that does not response to intensity outliers. With the outliers suppressed, our solution behaves similarly to a maximum-likelihood solution in the presence of Gaussian noise. The effectiveness of our algorithm is demonstrated with super-resolution restoration of real infrared image sequences under severe aliasing and intensity outliers.

Super-Resolution Reconstruction Based on Generalized Huber-MRF Image Modeling

Super-Resolution (SR) reconstruction has been a very hot research topic currently. A kind of generalized MRF (GMRF, generalized Markov random field) models is firstly proposed based on the recently reported bilateral filtering. The GMRF model is not only edge-preserving and robust to noise, inherited directly from the bilateral filtering, but also connects the bilateral filtering with the Bayesian MAP (maximum a posterior) approaches much concisely. Meanwhile, an improved numerical scheme of anisotropic diffusion PDE's (partial differential equation) is deduced based on the GMRF model. In the MRF-MAP framework, a new SR restoration algorithm is subsequently proposed for both cases of Gaussian noise and impulse noise, utilizing the generalized Huber-MRF model which guarantees strictly global convergence. The half-quadratic regularization approach and steepest descent are exploited to solve the energy functional. Experimental results demonstrate the effectiveness of this approach, both in the visual effect and the PSNR value.

On Ambiguities in Super-Resolution Modeling

Some models for super-resolution restoration assume that low-resolution (LR) images are formed from high-resolution (HR) ones by a warping first, blurring second (followed by decimating) imaging process; whereas others adopt a blurring first, warping second imaging constraint. These models are abused to some extent and respective conditions for usability of them are not identified. Such ambiguities are analyzed and cleared up in this letter. Conclusions are: The warping-blurring model coincides with the general imaging physics, but it is usable only if the motion among HR images is known a priori. When the motion is estimated from LR images, this model may cause systematic error, but the use of the blurring-warping model is more appropriate, and leads to better performance.

Computer vision applied to super resolution

Super-resolution (SR) restoration aims to solve the following problem: given a set of observed images, estimate an image at a higher resolution than is present in any of the individual images. Where the application of this technique differs in computer vision from other fields is in the variety and severity of the registration transformation between the images. In particular this transformation is generally unknown, and a significant component of solving the SR problem in computer vision is the estimation of the transformation. The transformation may have a simple parametric form, or it may be scene dependent and have to be estimated for every point. In either case the transformation is estimated directly and automatically from the images. We describe the two key components that are necessary for successful SR restoration: the accurate alignment or registration of the LR images and the formulation of an SR estimator that uses a generative image model together with a prior model of the super-resolved image itself. As with many other problems in computer vision, these different aspects are tackled in a robust, statistical framework.

An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations

This paper presents a new technique for generating a high resolution image from a blurred image sequences this is also referred to as super-resolution restoration of images. The image sequence consists of decimated, blurred and noisy versions of the high resolution image. The high resolution image is modeled as a Markov random field (MRF) and a maximum a posteriori (MAP) estimation technique is used for super-resolution restoration. Unlike other super-resolution imaging methods, the proposed technique does not require sub-pixel registration of given observations. A simple gradient descent method is used to optimize the functional. The discontinuities in the intensity process can be preserved by introducing suitable line processes. Superiority of this technique to standard methods of image expansion like pixel replication and spline interpolation is illustrated.

Superresolution restoration of an image sequence: adaptive filtering approach

This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters, least mean squares (LMS) or recursive least squares (RLS). The adaptation enables the treatment of linear space and time-variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the superresolution restoration algorithms are presented.

Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images

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.

N topics in search of an editorial: Heuristics, superresolution, and bibliography

This paper is the result of editorial license in the effect that it is designed to augment the invited papers for enhancement and restoration in this issue. Unfortunately this editor found the genesis of a special issue to be a very dynamic process, and as such this paper has experienced many contortions. However, it is hoped that a few topics not covered earlier, but which warrant some discussion in a digital image enhancement issue, can find a home (albeit brief and uneasy) in this editorial. The topics discussed below include some heuristic techniques which might be useful to the image enhancement objective, some superresolution and positive restoration techniques (still of esoteric interest), some comments on experimental methods, and a bibliography restricted to digital techniques for image restoration and/or enhancement.