Pan-denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total Variation

• URL: http://arxiv.org/abs/2407.06064v2
• Date: Mon, 9 Sep 2024 08:34:03 GMT
• Title: Pan-denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total Variation
• Authors: Shuang Xu, Qiao Ke, Jiangjun Peng, Xiangyong Cao, Zixiang Zhao,
• Abstract summary: This paper introduces a novel paradigm for hyperspectral image (HSI) denoising, which is termed textitpan-denoising. Panchromatic (PAN) images capture similar structures and textures to HSIs but with less noise. Consequently, pan-denoising has the potential to uncover underlying structures and details beyond the internal information modeling of traditional HSI denoising methods. Experiments on synthetic and real-world datasets demonstrate that PWRCTV outperforms several state-of-the-art methods in terms of metrics and visual quality.
• Score: 20.240211073097758
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel paradigm for hyperspectral image (HSI) denoising, which is termed \textit{pan-denoising}. In a given scene, panchromatic (PAN) images capture similar structures and textures to HSIs but with less noise. This enables the utilization of PAN images to guide the HSI denoising process. Consequently, pan-denoising, which incorporates an additional prior, has the potential to uncover underlying structures and details beyond the internal information modeling of traditional HSI denoising methods. However, the proper modeling of this additional prior poses a significant challenge. To alleviate this issue, the paper proposes a novel regularization term, Panchromatic Weighted Representation Coefficient Total Variation (PWRCTV). It employs the gradient maps of PAN images to automatically assign different weights of TV regularization for each pixel, resulting in larger weights for smooth areas and smaller weights for edges. This regularization forms the basis of a pan-denoising model, which is solved using the Alternating Direction Method of Multipliers. Extensive experiments on synthetic and real-world datasets demonstrate that PWRCTV outperforms several state-of-the-art methods in terms of metrics and visual quality. Furthermore, an HSI classification experiment confirms that PWRCTV, as a preprocessing method, can enhance the performance of downstream classification tasks. The code and data are available at https://github.com/shuangxu96/PWRCTV.

Related papers

• Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformer [17.430622649002427]
  Existing learning-based denoising methods typically train models to generalize the image prior from large-scale datasets. We propose a new perspective on the denoising challenge by highlighting the distinct separation between noise and image priors. We introduce a Locally Noise Prior Estimation algorithm, which accurately estimates the noise prior directly from a single raw noisy image.
  arXiv  Detail & Related papers  (2024-07-12T08:43:11Z)
• Gradpaint: Gradient-Guided Inpainting with Diffusion Models [71.47496445507862]
  Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved remarkable results in conditional and unconditional image generation. We present GradPaint, which steers the generation towards a globally coherent image. We generalizes well to diffusion models trained on various datasets, improving upon current state-of-the-art supervised and unsupervised methods.
  arXiv  Detail & Related papers  (2023-09-18T09:36:24Z)
• A Novel Truncated Norm Regularization Method for Multi-channel Color Image Denoising [5.624787484101139]
  This paper is proposed to denoise color images with a double-weighted truncated nuclear norm minus truncated Frobenius norm minimization (DtNFM) method. Through exploiting the nonlocal self-similarity of the noisy image, the similar structures are gathered and a series of similar patch matrices are constructed. Experiments on synthetic and real noise datasets demonstrate that the proposed method outperforms many state-of-the-art color image denoising methods.
  arXiv  Detail & Related papers  (2023-07-16T03:40:35Z)
• Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling [56.506240377714754]
  We present a novel strategy called the Diffusion Model for Image Denoising (DMID) Our strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained unconditional diffusion model. Our DMID strategy achieves state-of-the-art performance on both distortion-based and perception-based metrics.
  arXiv  Detail & Related papers  (2023-07-08T14:59:41Z)
• Enhancing convolutional neural network generalizability via low-rank weight approximation [6.763245393373041]
  Sufficient denoising is often an important first step for image processing. Deep neural networks (DNNs) have been widely used for image denoising. We introduce a new self-supervised framework for image denoising based on the Tucker low-rank tensor approximation.
  arXiv  Detail & Related papers  (2022-09-26T14:11:05Z)
• Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising [104.59305271099967]
  We present a pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising. We develop a pixel aggregation network for video denoising to sample pixels across the spatial-temporal space. Our method is able to solve the misalignment issues caused by large motion in dynamic scenes.
  arXiv  Detail & Related papers  (2021-01-26T13:00:46Z)
• SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising [10.597014770267672]
  Deep learning (DL) based hyperspectral images (HSIs) denoising approaches directly learn the nonlinear mapping between observed noisy images and underlying clean images. We introduce a novel model guided interpretable network for HSI denoising.
  arXiv  Detail & Related papers  (2020-12-03T11:05:01Z)
• Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration [66.68541690283068]
  We propose a unified paradigm combining the spatial and spectral properties for hyperspectral image restoration. The proposed paradigm enjoys performance superiority from the non-local spatial denoising and light computation complexity. Experiments on HSI denoising, compressed reconstruction, and inpainting tasks, with both simulated and real datasets, demonstrate its superiority.
  arXiv  Detail & Related papers  (2020-10-24T15:53:56Z)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.