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Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed. 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(More)
In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key(More)
We address the dynamic super-resolution (SR) problem of reconstructing a high-quality set of monochromatic or color super-resolved images from low-quality monochromatic, color, or mosaiced frames. Our approach includes a joint method for simultaneous SR, deblurring, and demosaicing, this way taking into account practical color measurements encountered in(More)
— Multiframe image reconstruction produces images beyond the native resolution of a digital image sensor by way of accurate sub-pixel registration of aliased images. We present a novel multiframe registration approach for the purpose of enhancing resolution of digital mammogram images. We demonstrate the ability to improve resolution while maintaining(More)
Super-Resolution reconstruction produces one or a set of high-resolution images from a sequence of low-resolution frames. This article reviews a variety of Super-Resolution methods proposed in the last 20 years, and provides some insight into, and a summary of, our recent contributions to the general Super-Resolution problem. In the process, a detailed(More)
In the last two decades, two related categories of problems have been studied independently in 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(More)
Kernel regression is an effective tool for a variety of image processing tasks such as denoising and interpolation [1]. In this paper, we extend the use of kernel regression for deblurring applications. In some earlier examples in the literature, such nonparametric deblurring was suboptimally performed in two sequential steps, namely denoising followed by(More)
In the last two decades, many papers have been published, proposing a variety of methods for multi-frame 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. Different implementations of the non-iterative(More)
Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory(More)
who is the principal architect of the code, with significant contributions from Dirk Robinson, particularly on the motion estimation algorithms. This software package is available for licensing. Please contact Prof. Peyman Milanfar (milanfar@ee.ucsc.edu) if you wish to obtain this code for research or commercial purposes. Abstract This software suite is a(More)