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• W2891878684 abstract "In the classical bilateral filter, a fixed Gaussian range kernel is used along with a spatial kernel for edge-preserving smoothing. We consider a generalization of this filter, the so-called adaptive bilateral filter, where the center and width of the Gaussian range kernel is allowed to change from pixel to pixel. Though this variant was originally proposed for sharpening and noise removal, it can also be used for other applications such as artifact removal and texture filtering. Similar to the bilateral filter, the brute-force implementation of its adaptive counterpart requires intense computations. While several fast algorithms have been proposed in the literature for bilateral filtering, most of them work only with a fixed range kernel. In this paper, we propose a fast algorithm for adaptive bilateral filtering, whose complexity does not scale with the spatial filter width. This is based on the observation that the concerned filtering can be performed purely in range space using an appropriately defined local histogram. We show that by replacing the histogram with a polynomial and the finite range-space sum with an integral, we can approximate the filter using analytic functions. In particular, an efficient algorithm is derived using the following innovations: the polynomial is fitted by matching its moments to those of the target histogram (this is done using fast convolutions), and the analytic functions are recursively computed using integration-by-parts. Our algorithm can accelerate the brute-force implementation by at least $20 times$, without perceptible distortions in the visual quality. We demonstrate the effectiveness of our algorithm for sharpening, JPEG deblocking, and texture filtering." @default.
• W2891878684 created "2018-09-27" @default.
• W2891878684 creator A5045035201 @default.
• W2891878684 creator A5062644223 @default.
• W2891878684 date "2019-02-01" @default.
• W2891878684 modified "2023-10-18" @default.
• W2891878684 title "Fast Adaptive Bilateral Filtering" @default.
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• W2891878684 doi "https://doi.org/10.1109/tip.2018.2871597" @default.
• W2891878684 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30235131" @default.
• W2891878684 hasPublicationYear "2019" @default.
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