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Accelerating block-matching and 3D filtering-based image denoising algorithm on FPGAs

Authors: Xiaoyun Wang; Ke Xu; Dong Wang;

Accelerating block-matching and 3D filtering-based image denoising algorithm on FPGAs

Abstract

Image denoising is an important pre-processing step in the field of computer vision. And the block-matching and 3D filtering (BM3D) algorithm has achieved quite impressive results in the terms of denoising quality, but it is not being widely used due to its computational complexity. In this paper, we consider FPGA as accelerator because of its reconfigurability and advantage on energy efficiency over GPU. We present a OpenCL-Based FPGA implementation which increased the image processing speed significantly. At the same time, the denoising quality of the image is not affected too much. To our knowledge, this is the first successful attempt of the BM3D algorithm on FPGAs. Also, our implementation is more fine-grained partition of the algorithm, so some parts of our algorithmic, such as block-matching, can be utilized separately to other similar algorithms.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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