Color Image Denoising via Cross-Channel Texture Transferring

Dinh, Khanh Quoc; Canh, Thuong Nguyen

doi:10.1109/lsp.2016.2580711

Cited by 5 publications

(4 citation statements)

References 20 publications

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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%

“…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. Meanwhile, the guided filter is employed to remove noise in the noisy luma channel.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Multi-Spectral Fusion and Denoising of Color and Near-Infrared Images Using Multi-Scale Wavelet Analysis

Jung

2021

Sensors

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We formulate multi-spectral fusion and denoising for the luminance channel as a maximum a posteriori estimation problem in the wavelet domain. To deal with the discrepancy between RGB and near infrared (NIR) data in fusion, we build a discrepancy model and introduce the wavelet scale map. The scale map adjusts the wavelet coefficients of NIR data to have the same distribution as the RGB data. We use the priors of the wavelet scale map and its gradient as the contrast preservation term and gradient denoising term, respectively. Specifically, we utilize the local contrast and visibility measurements in the contrast preservation term to transfer the selected NIR data to the fusion result. We also use the gradient of NIR wavelet coefficients as the weight for the gradient denoising term in the wavelet scale map. Based on the wavelet scale map, we perform fusion of the RGB and NIR wavelet coefficients in the base and detail layers. To remove noise, we model the prior of the fused wavelet coefficients using NIR-guided Laplacian distributions. In the chrominance channels, we remove noise guided by the fused luminance channel. Based on the luminance variation after fusion, we further enhance the color of the fused image. Our experimental results demonstrated that the proposed method successfully performed the fusion of RGB and NIR images with noise reduction, detail preservation, and color enhancement.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

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Multi-Spectral Fusion and Denoising of Color and Near-Infrared Images Using Multi-Scale Wavelet Analysis

Jung

2021

Sensors

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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%

Pruned non‐local means

Ghosh¹,

Mandal²,

Chaudhury³

2017

IET Image Processing

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In Non-Local Means (NLM), each pixel is denoised by performing a weighted averaging of its neighboring pixels, where the weights are computed using image patches. We demonstrate that the denoising performance of NLM can be improved by pruning the neighboring pixels, namely, by rejecting neighboring pixels whose weights are below a certain threshold λ. While pruning can potentially reduce pixel averaging in uniform-intensity regions, we demonstrate that there is generally an overall improvement in the denoising performance. In particular, the improvement comes from pixels situated close to edges and corners. The success of the proposed method strongly depends on the choice of the global threshold λ, which in turn depends on the noise level and the image characteristics. We show how Stein's unbiased estimator of the mean-squared error can be used to optimally tune λ, at a marginal computational overhead. We present some representative denoising results to demonstrate the superior performance of the proposed method over NLM and its variants.

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Color image restoration by filtering methods: a review

Salamat,

Missen,

Akhtar

et al. 2024

Soft Comput

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Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switching and wavelet filtering methods. These classes are based on the particular methodology used in image denoising algorithms and further subdivided to show their classification in terms of noise models utilized, application style, and stages the filters applied in images. In particular, we present a review of filtering methods in color image denoising, published over the past two decades. Our classification and succinct descriptions of color image restoration by these mathematical filtering techniques and their characterizations can help choose the appropriate ones for various downstream image processing tasks.

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Color Image Denoising via Cross-Channel Texture Transferring

Cited by 5 publications

References 20 publications

Multi-Spectral Fusion and Denoising of Color and Near-Infrared Images Using Multi-Scale Wavelet Analysis

Multi-Spectral Fusion and Denoising of Color and Near-Infrared Images Using Multi-Scale Wavelet Analysis

Pruned non‐local means

Color image restoration by filtering methods: a review

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