2016
DOI: 10.1109/lsp.2016.2580711
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Color Image Denoising via Cross-Channel Texture Transferring

Abstract: Image denoising can reduce the perturbation inevitably generated during image signal acquisition and its subsequent processing. While the utilization of nonlocal properties can enhance the performance of the state-of-the-art denoising me thods, a heavy computational burden is incurred especially for color images. Inspired by the high correlation in the texture information over color channels, for a reduction of the computational burden, this letter proposes denoising the luma channel first and then performing … Show more

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Cited by 4 publications
(4 citation statements)
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“…In Section IV.B, we obtained the fused luminance image by combining RGB and NIR images. Then, we employed the guided image filter [ 28 ] to denoise the chrominance channels guided by the fused luminance results [ 29 ]. The primary idea of [ 29 ] is the high correlation in the texture information over color channels.…”
Section: Proposed Methodsmentioning
confidence: 99%
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“…In Section IV.B, we obtained the fused luminance image by combining RGB and NIR images. Then, we employed the guided image filter [ 28 ] to denoise the chrominance channels guided by the fused luminance results [ 29 ]. The primary idea of [ 29 ] is the high correlation in the texture information over color channels.…”
Section: Proposed Methodsmentioning
confidence: 99%
“…Then, we employed the guided image filter [ 28 ] to denoise the chrominance channels guided by the fused luminance results [ 29 ]. The primary idea of [ 29 ] is the high correlation in the texture information over color channels. Based on the correlation, [ 29 ] transfers the texture information of the fused luma channel to the other two channels and removes the noise around the texture by the guided filter.…”
Section: Proposed Methodsmentioning
confidence: 99%
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“…The primary application of NLM is noise reduction [14], [15]. However, since the inception of NLM, more sophisticated algorithms with superior denoising performances have been proposed [16], [17], [18], [19], [20], [21], [22], [23], [24]. While NLM and its variants are no longer the stateof-the-art in image denoising, they nevertheless continue to be of interest due to their simplicity, decent denoising performance, and the availability of fast implementations.…”
Section: Introductionmentioning
confidence: 99%