In the last two decades, many papers have been published, proposing a variety of methods for multi- frame resolution enhancement. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose an alternate approach using L norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation, and results in images with sharp edges.

Super-resolution reconstruction produces one or a set of high-resolution images from a sequence of low-resolution frames. This paper reviews a variety of super-resolution methods proposed in the last twenty years, and provides some insight to, and a summary of, our recent contributions to the general super-resolution problem. In the process, a detailed study of several very important aspects of super-resolution, often ignored in the literature, is presented. Specifically, we discuss robustness, treatment of color, and dynamic operation modes. Novel methods for addressing these issues are accompanied by experimental results on simulated and real data. Finally, some future challenges in super-resolution are outlined and discussed.

In the last two decades, two related categories of problems have been studied independently in the image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a MAP estimation technique by minimizing a multi-term cost function. The L 1 norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional regularization term is used to force similar edge location and orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of

We propose a novel approach to reduce spatially varying motion blur using a hybrid camera system that simultaneously captures high-resolution video at a low-frame rate together with low-resolution video at a high-frame rate. Our work is inspired by Ben-Ezra and Nayar [3] who introduced the hybrid camera idea for correcting global motion blur for a single still image. We broaden the scope of the problem to address spatially varying blur as well as video imagery. We also reformulate the correction process to use more information available in the hybrid camera system, as well as iteratively refine spatially varying motion extracted from the low-resolution high-speed camera. We demonstrate that our approach achieves superior results over existing work and can be extended to deblurring of moving objects. 1.

We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to a local Taylor series expansion. Unlike the traditional framework, however, the window function of adaptive NC is adapted to local linear structures. This leads to more samples of the same modality being gathered for the analysis, which in turn improves signal-to-noise ratio and reduces diffusion across discontinuities. A robust signal certainty is also adapted to the sample intensities to minimize the influence of outliers. Excellent fusion capability of adaptive NC is demonstrated through an application of super-resolution image reconstruction. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved. 1.

resolution enhancement. These methods, which have a wide range of complexity, memory and time requirements, are usually very sensitive to their assumed model of data and noise, often limiting their utility. Di#erent implementations of the non-iterative Shift and Add concept have been proposed as very fast and e#ective superresolution algorithms. The paper of Elad & Hel-Or 2001 provided an adequate mathematical justification for the Shift and Add method for the simple case of an additive Gaussian noise model. In this paper we prove that additive Gaussian distribution is not a proper model for super-resolution noise. Specifically, we show that L p norm minimization (1 2) results in a pixelwise weighted mean algorithm which requires the least possible amount of computation time and memory and produces a maximum likelihood solution. We also justify the use of a robust prior information term based on bilateral filter idea. Finally, for the underdetermined case, where the number of non-redundant low-resolution frames are less than square of the resolution enhancement factor, we propose a method for detection and removal of outlier pixels. Our experiments using commercial digital cameras show that our proposed super-resolution method provides significant improvements in both accuracy and e#ciency.

Figure 1. From left to right: Light field image captured with a plenoptic camera (detail); the light field image on the left is rearranged as a collection of several views; central view extracted from the light field, with one pixel per microlens, as in a traditional rendering [23]; central view superresolved with our method. Light field cameras have been recently shown to be very effective in applications such as digital refocusing and 3D reconstruction. In a single snapshot these cameras provide a sample of the light field of a scene by trading off spatial resolution with angular resolution. Current methods produce images at a resolution that is much lower than that of traditional imaging devices. However, by explicitly modeling the image formation process and incorporating priors such as Lambertianity and texture statistics, these types of images can be reconstructed at a higher resolution. We formulate this method in a variational Bayesian framework and perform the reconstruction of both the surface of the scene and the (superresolved) light field. The method is demonstrated on both synthetic and real images captured with our light-field camera prototype. 1.

We describe a novel approach to reduce spatially-varying motion blur in video and images using a hybrid camera system. A hybrid camera is a standard video camera that is coupled with an auxiliary low-resolution camera sharing the same optical path but capturing at a significantly higher frame rate. The auxiliary video is temporally sharper but at a lower resolution, while the lower-frame-rate video has higher spatial resolution but is susceptible to motion blur. Our deblurring approach uses the data from these two video streams to reduce spatially-varying motion blur in the high-resolution camera with a technique that combines both deconvolution and superresolution. Our algorithm also incorporates a refinement of the spatially-varying blur kernels to further improve results. Our approach can reduce motion blur from the high-resolution video as well as estimate new high-resolution frames at a higher framerate. Experimental results on a variety of inputs demonstrate notable improvement over current state-of-the-art methods in image/video deblurring.

Time-of-flight (TOF) cameras robustly provide depth data of real world scenes at video frame rates. Unfortunately, currently available camera models provide rather low X-Y resolution. Also, their depth measurements are starkly influenced by random and systematic errors which renders them inappropriate for high-quality 3D scanning. In this paper we show that ideas from traditional color image superresolution can be applied to TOF cameras in order to obtain 3D data of higher X-Y resolution and less noise. We will also show that our approach, which works using depth images only, bears many advantages over alternative depth upsampling methods that combine information from separate high-resolution color and low-resolution depth data. 1.

Abstract. This paper addresses the problem of super resolution- obtaining a single high-resolution image given a set of low resolution images which are related by small displacements. We employ a reconstruction based approach using MRF-MAP formalism, and use approximate optimization using graph cuts to carry out the reconstruction. We also use the same formalism to investigate high resolution expansions from single images by deconvolution assuming that the point spread function is known. We present a method for the estimation of the point spread function for a given camera. Our results demonstrate that it is possible to obtain super-resolution preserving high frequency details well beyond the predicted limits of magnification. 1