An Efficient and Scalable Image Filtering Framework Using VIPS Fusion
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Date
2013
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Publisher
The Eurographics Association and Blackwell Publishing Ltd.
Abstract
Edge-preserving image filtering is a valuable tool for a variety of applications in image processing and computer vision. Motivated by a new simple but effective local Laplacian filter, we propose a scalable and efficient image filtering framework to extend this edge-preserving image filter and construct an uniform implementation in O(N) time. The proposed framework is built upon a practical global-to-local strategy. The input image is first remapped globally by a series of tentative remapping functions to generate a virtual candidate image sequence (Virtual Image Pyramid Sequence, VIPS). This sequence is then recombined locally to a single output image by a flexible edge-aware pixel-level fusion rule. To avoid halo artifacts, both the output image and the virtual candidate image sequence are transformed into multi-resolution pyramid representations. Four examples, single image de-hazing, multi-exposure fusion, fast edge-preserving filtering and tone-mapping, are presented as the concrete applications of the proposed framework. Experiments on filtering effect and computational efficiency indicate that the proposed framework is able to build a wide range of fast image filtering that yields visually compelling results.
Description
@article{:10.1111/cgf.12247,
journal = {Computer Graphics Forum},
title = {{An Efficient and Scalable Image Filtering Framework Using VIPS Fusion}},
author = {Zhang, Jun and Chen, Xiuhong and Zhao, Yan and Li, H.},
year = {2013},
publisher = {The Eurographics Association and Blackwell Publishing Ltd.},
ISSN = {1467-8659},
DOI = {/10.1111/cgf.12247}
}