Lp-norm-based successive denoising approach for hyperspectral images

Maji, Suman Kumar; Aetesam, Hazique

doi:10.1080/2150704x.2023.2195567

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…In order to recover more image details, Shen et al [33] combined the wavelet frame to establish the non-convex L p regularization image deblurring model. A novel continuous denoising strategy that relied on the L p -norm for the removal of mixed Gaussian impulse noise from hyperspectral images [21].…”

Section: P -Norm Regularizationmentioning

confidence: 99%

Blind image deblurring using kernel error for <i>p</i>-shrinkage operator optimization model

Wu,

Feng,

2024

IPI

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The field of blind image deblurring has been broadly investigated and has yielded significant achievements. The two-stage method has been widely adopted for blind image deblurring. In particular, the accurate estimation of the blur kernel during the first stage is critical to overall success. Nevertheless, many existing methods may not accomplish sufficient accuracy in the blur kernel estimation, leading to restored images that exhibit boundary distortions and other undesirable artifacts in the second stage. In this paper, a robust blind image deblurring model is proposed, which addresses the inherent uncertainty in the blur kernel estimation during the blind deblurring, decomposing the blur kernel into a deterministic component and a random component for the purpose of reducing the impact of the kernel error on image restoration through iterative estimation. Furthermore, the utilization of the Lp-norm in image restoration has demonstrated exceptional performance, therefore, the Lp-norm is utilized to achieve optimal restored images. The effectiveness of the proposed method is investigated through quantitative and qualitative experimental evaluations, which demonstrate its superior performance compared to state-of-the-art methods in benchmark datasets and text images, as well as natural degradation images.

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Section: P -Norm Regularizationmentioning

confidence: 99%

Blind image deblurring using kernel error for <i>p</i>-shrinkage operator optimization model

Wu,

Feng,

2024

IPI

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Influence of sorting measures on similar segment grouping based denoising algorithms

Preethisree,

Vivekanand,

Mishra

et al. 2023

SIViP

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Hyperspectral image restoration using noise gradient and dual priors under mixed noise conditions

Aetesam,

Maji,

Prasath

2024

CAAI Trans on Intel Tech

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Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum. However, due to sensor limitations and imperfections during the image acquisition and transmission phases, noise is introduced into the acquired image, which can have a negative impact on downstream analyses such as classification, target tracking, and spectral unmixing. Noise in hyperspectral images (HSI) is modelled as a combination from several sources, including Gaussian/impulse noise, stripes, and deadlines. An HSI restoration method for such a mixed noise model is proposed. First, a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian‐impulse noise by estimating both the clean data as well as the sparse/impulse noise levels. Second, a hyper‐Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image gradients. Third, to model the sparse nature of impulse noise, an ℓ1 − norm over the impulse noise gradient is used. Because the proposed methodology employs two distinct priors, the authors refer to it as the hyperspectral dual prior (HySpDualP) denoiser. To the best of authors' knowledge, this joint optimisation framework is the first attempt in this direction. To handle the non‐smooth and non‐convex nature of the general ℓp − norm‐based regularisation term, a generalised shrinkage/thresholding (GST) solver is employed. Finally, an efficient split‐Bregman approach is used to solve the resulting optimisation problem. Experimental results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’ proposed model outperforms state‐of‐the‐art methods, both visually and in terms of various image quality assessment metrics.

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Lp-norm-based successive denoising approach for hyperspectral images

Cited by 4 publications

References 25 publications

Blind image deblurring using kernel error for <i>p</i>-shrinkage operator optimization model

Blind image deblurring using kernel error for <i>p</i>-shrinkage operator optimization model

Influence of sorting measures on similar segment grouping based denoising algorithms

Hyperspectral image restoration using noise gradient and dual priors under mixed noise conditions

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