Denoising of Multichannel Images With Nonlinear Transformation of Reference Image

Abramov, Sergey; Abramova, Victoriya; Егиазарян, Карен

doi:10.1615/telecomradeng.v77.i9.30

Cited by 3 publications

(9 citation statements)

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“…Also, note that it is possible to have two or more modified reference images after a three-point or more DCT applied in the vertical direction to decorrelate data. We considered two reference images instead of one in the work of [31], and filtering has occurred to be more efficient in terms of standard metrics, such as output PSNR, and visual quality metrics, such as PSNR-HVS-M, which takes into account two important properties of human vision system (HVS), namely, less sensitivity to distortions in high frequency components and masking (M) effect of image texture and other heterogeneities [40]. Besides, after two-or three-point DCT, it is possible to apply component-wise different filters including standard DCT, BM3D, or others.…”

Section: Ijmentioning

confidence: 99%

“…Usually, if a given filter is more efficient in component-wise (single-channel) denoising, its use is also beneficial in the considered denoising with a reference [30]. It is also worth stressing that optimal (recommended) parameters of thresholds applied in DCT coefficient thresholding have been determined in the literature [24,30,31]. These thresholds differ from those usually recommended for the cases in which these filters are employed for noise removal in single channel images.…”

Section: Ijmentioning

confidence: 99%

“…We start analyzing the performance of methods of image filtering with reference(s) for simulated data [24,30,31]. In our simulations, four test images typical for remote sensing, presented in Figure 4 and denoted as FR01, FR02, FR03, and FR04, and two high quality component images denoted RS1 and RS2 of AVIRIS hypercube of data were used.…”

Section: Performance Criteriamentioning

confidence: 99%

“…However, similar to and exploit some information from the reference (for example, about positions of edges [22]) or to incorporate reference image(s) into processing directly. Important items here are to find a proper reference and to make it as "close" to the noisy image as possible (e.g., by appropriate nonlinear transformation [31]). The approach [24,30,31] allows using both DCT [15] and BM3D [32] filters as well as to easily cope with signal-dependent noise in the component image to be denoised by applying a proper variance stabilizing transform (VST) to it before filtering.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of Component Images of Multispectral Data by Denoising with Reference

et al. 2019

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Multispectral remote sensing data may contain component images that are heavily corrupted by noise and the pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images—component images having relatively high quality and that are similar to the image subject to pre-filtering. Here, we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. To provide high efficiency of denoising, reference images have to be transformed using linear or nonlinear transformations. This paper proposes a practical approach to doing this. Examples of denoising tests and real-life images demonstrate high efficiency of the proposed approach.

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Section: Ijmentioning

confidence: 99%

Section: Ijmentioning

confidence: 99%

Section: Performance Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancement of Component Images of Multispectral Data by Denoising with Reference

et al. 2019

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“…The main progress and benefits result from the fact that similar patches that can be used in collaborative denoising can be found not only in a given component image, but also in other component images. Other positive outcomes result from the fact that, in multichannel RS data, there can be almost noise-free component images (called references) that are quite similar to a noisy component image that needs enhancement [22,23,24,[30][31]. The main ideas are either to retrieve and exploit some information from the reference (for example, about positions of edges [22]) or to incorporate reference image(s) into processing directly.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Abramov¹,

Uss²,

Vozel³

et al. 2019

Preprint

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Multispectral remote sensing data may contain component images which are heavily corrupted by noise and pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images – component images having relatively high quality and which are similar to the image subject to pre-filtering. Here we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. Examples of denoising of real-life images demonstrate high efficiency of the proposed approach.

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Lossy Compression of Single-channel Noisy Images by Modern Coders

Kryvenko,

Lukin,

Vozel

2024

Remote Sensing

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Lossy compression of remote-sensing images is a typical stage in their processing chain. In design or selection of methods for lossy compression, it is commonly assumed that images are noise-free. Meanwhile, there are many practical situations where an image or a set of its components are noisy. This fact needs to be taken into account since noise presence leads to specific effects in lossy compressed data. The main effect is the possible existence of the optimal operation point (OOP) shown for JPEG, JPEG2000, some coders based on the discrete cosine transform (DCT), and the better portable graphics (BPG) encoder. However, the performance of such modern coders as AVIF and HEIF with application to noisy images has not been studied yet. In this paper, analysis is carried out for the case of additive white Gaussian noise. We demonstrate that OOP can exist for AVIF and HEIF and the performance characteristics in it are quite similar to those for the BPG encoder. OOP exists with a higher probability for images of simpler structure and/or high-intensity noise, and this takes place according to different metrics including visual quality ones. The problems of providing lossy compression by AVIF or HEIF are shown and an initial solution is proposed. Examples for test and real-life remote-sensing images are presented.

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Denoising of Multichannel Images With Nonlinear Transformation of Reference Image

Cited by 3 publications

References 0 publications

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Lossy Compression of Single-channel Noisy Images by Modern Coders

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