47,651 research outputs found

    Adaptive Nonlocal Filtering: A Fast Alternative to Anisotropic Diffusion for Image Enhancement

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    The goal of many early visual filtering processes is to remove noise while at the same time sharpening contrast. An historical succession of approaches to this problem, starting with the use of simple derivative and smoothing operators, and the subsequent realization of the relationship between scale-space and the isotropic dfffusion equation, has recently resulted in the development of "geometry-driven" dfffusion. Nonlinear and anisotropic diffusion methods, as well as image-driven nonlinear filtering, have provided improved performance relative to the older isotropic and linear diffusion techniques. These techniques, which either explicitly or implicitly make use of kernels whose shape and center are functions of local image structure are too computationally expensive for use in real-time vision applications. In this paper, we show that results which are largely equivalent to those obtained from geometry-driven diffusion can be achieved by a process which is conceptually separated info two very different functions. The first involves the construction of a vector~field of "offsets", defined on a subset of the original image, at which to apply a filter. The offsets are used to displace filters away from boundaries to prevent edge blurring and destruction. The second is the (straightforward) application of the filter itself. The former function is a kind generalized image skeletonization; the latter is conventional image filtering. This formulation leads to results which are qualitatively similar to contemporary nonlinear diffusion methods, but at computation times that are roughly two orders of magnitude faster; allowing applications of this technique to real-time imaging. An additional advantage of this formulation is that it allows existing filter hardware and software implementations to be applied with no modification, since the offset step reduces to an image pixel permutation, or look-up table operation, after application of the filter

    High spatial resolution and high contrast optical speckle imaging with FASTCAM at the ORM

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    In this paper, we present an original observational approach, which combines, for the first time, traditional speckle imaging with image post-processing to obtain in the optical domain diffraction-limited images with high contrast (1e-5) within 0.5 to 2 arcseconds around a bright star. The post-processing step is based on wavelet filtering an has analogy with edge enhancement and high-pass filtering. Our I-band on-sky results with the 2.5-m Nordic Telescope (NOT) and the lucky imaging instrument FASTCAM show that we are able to detect L-type brown dwarf companions around a solar-type star with a contrast DI~12 at 2" and with no use of any coronographic capability, which greatly simplifies the instrumental and hardware approach. This object has been detected from the ground in J and H bands so far only with AO-assisted 8-10 m class telescopes (Gemini, Keck), although more recently detected with small-class telescopes in the K band. Discussing the advantage and disadvantage of the optical regime for the detection of faint intrinsic fluxes close to bright stars, we develop some perspectives for other fields, including the study of dense cores in globular clusters. To the best of our knowledge this is the first time that high contrast considerations are included in optical speckle imaging approach.Comment: Proceedings of SPIE conference - Ground-based and Airborne Instrumentation for Astronomy III (Conference 7735), San Diego 201

    Detection of dirt impairments from archived film sequences : survey and evaluations

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    Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research
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