Digital photography with flash and no-flash image pairs

Petschnigg, Georg; Szeliski, Richard; Agrawala, Maneesh; Cohen, Michael F.; Hoppe, Hugues; Toyama, Kentaro

doi:10.1145/1186562.1015777

Cited by 337 publications

(391 citation statements)

References 30 publications

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“…To this end, an effective algorithm is presented to smooth the probability maps by employing the framework of bilateral filter. The technique of JBF was proposed by Petschnigg et al [45] as an extension of the bilateral filter. In this work, we extend a JBF for probability maps while using the original hyperspectral image as a guide to compute the range weights G σ r , instead of the probability maps.…”

Section: Joint Bilateral Filtermentioning

confidence: 99%

Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model

2016

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Abstract:Hyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In this method, a novel technique for labeling regions obtained by the spectral-spatial segmentation process is presented. Our method includes the following steps. First, the probabilistic support vector machines (SVM) classifier is used to estimate probabilities belonging to each information class. Second, an extended JBF is employed to perform image smoothing on the probability maps. By using our JBF process, salt-and-pepper classification noise in homogeneous regions can be effectively smoothed out while object boundaries in the original image are better preserved as well. Third, a sequence of modified bi-labeling graph cut models is constructed for each information class to extract the desirable object belonging to the corresponding class from the smoothed probability maps. Finally, a classification map is achieved by merging the segmentation maps obtained in the last step using a simple and effective rule. Experimental results based on three benchmark airborne hyperspectral datasets with different resolutions and contexts demonstrate that our method can achieve 8.56%-13.68% higher overall accuracies than the pixel-wise SVM classifier. The performance of our method was further compared to several classical hyperspectral image classification methods using objective quantitative measures and a visual qualitative evaluation.

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Section: Joint Bilateral Filtermentioning

confidence: 99%

Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model

2016

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“…The intended applications can cover increasing the depth of field [47], separation of the illuminations, scene synthesis [48], relighting [49][50][51], adaptive color primaries, metamer detection, scene contrast enhancement, photography of fluorescent objects, high dynamic range photography [22], removing disturbances or dazzling [19], assisting with segmentation [49], image denoising or artifacts removal [10,52,53], image processing or editing (e.g. detail transfer, white-balancing, continuous flash and red-eye removal) [52,54,22], specular artifacts removal [55], estimation of the ambient illumination [56], depth estimation [57][58][59][60], light field transfer [48], shadow detection [61], and extracting middle-level features (e.g.…”

Section: Computational Samplingmentioning

confidence: 99%

An overview of computational photography

Suo

Dai

2012

Sci. China Inf. Sci.

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Computational photography is an emerging multidisciplinary field. Over the last two decades, it has integrated studies across computer vision, computer graphics, signal processing, applied optics and related disciplines. Researchers are exploring new ways to break through the limitations of traditional digital imaging for the benefit of photographers, vision and graphics researchers, and image processing programmers. Thanks to much effort in various associated fields, the large variety of issues related to these new methods of photography are described and discussed extensively in this paper. To give the reader the full picture of the voluminous literature related to computational photography, this paper briefly reviews the wide range of topics in this new field, covering a number of different aspects, including: (i) the various elements of computational imaging systems and new sampling and reconstruction mechanisms; (ii) the different image properties which benefit from computational photography, e.g. depth of field, dynamic range; and (iii) the sampling subspaces of visual scenes in the real world. Based on this systematic review of the previous and ongoing work in this field, we also discuss some open issues and potential new directions in computational photography. This paper aims to help the reader get to know this new field, including its history, ultimate goals, hot topics, research methodologies, and future directions, and thus build a foundation for further research and related developments.

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“…The cross bilateral filter, also called the joint bilateral filter, is a variation of the bilateral filter that filters an input image I in a manner that respects edges in another reference image J . It was introduced by Eisemann and Durand [17], Petschnigg et al [35], Kopf et al [24], and is formulated as follows: 2) with Eq. (2.1) and note the difference in the argument to the Gaussian.…”

Section: Bilateral Filter and Its Relativesmentioning

confidence: 99%

“…(2.1) and note the difference in the argument to the Gaussian. Smoothing I with respect to another source, namely J , allows not only fusing information from two images, as in the case of the flash-no-flash photography [17,35], but also filtering a dataset with respect to a distinct source of different resolution, like an image and a depth map [15,51]. Note that in this second case the sample J (y) must be prefiltered to match the sampling rate of I , whereas the sample J (x) is taken at J 's native resolution.…”

Section: Bilateral Filter and Its Relativesmentioning

confidence: 99%

Lattice-Based High-Dimensional Gaussian Filtering and the Permutohedral Lattice

2012

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High-dimensional Gaussian filtering is a popular technique in image processing, geometry processing and computer graphics for smoothing data while preserving important features. For instance, the bilateral filter, cross bilateral filter and non-local means filter fall under the broad umbrella of high-dimensional Gaussian filters. Recent algorithmic advances therein have demonstrated that by relying on a sampled representation of the underlying space, one can obtain speed-ups of orders of magnitude over the naïve approach. The simplest such sampled representation is a lattice, and it has been used successfully in the bilateral grid and the permutohedral lattice algorithms. In this paper, we analyze these lattice-based algorithms, developing a general theory of lattice-based high-dimensional Gaussian filtering. We consider the set of criteria for an optimal lattice for filtering, as it offers a good tradeoff of quality for computational efficiency, and evaluate the existing lattices under the criteria. In particular, we give a rigorous exposition of the properties of the permutohedral lattice and argue that it is the optimal lattice for Gaussian filtering. Lastly, we explore further uses of the permutohedral-lattice-based Gaussian filtering framework, showing that it can be easily adapted to perform mean shift filtering and yield improvement over the traditional approach based on a Cartesian grid.

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Digital photography with flash and no-flash image pairs

Cited by 337 publications

References 30 publications

Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model

Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model

An overview of computational photography

Lattice-Based High-Dimensional Gaussian Filtering and the Permutohedral Lattice

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