Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration

• URL: http://arxiv.org/abs/2407.08509v2
• Date: Mon, 16 Dec 2024 12:19:58 GMT
• Title: Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration
• Authors: Shuang Xu, Chang Yu, Jiangjun Peng, Xiangyong Cao, Deyu Meng,
• Abstract summary: This paper proposes a novel low-rank regularization term, named the Haar nuclear norm (HNN), for efficient and effective remote sensing image restoration. It leverages the low-rank properties of wavelet coefficients derived from the 2-D frontal slice-wise Haar discrete wavelet transform. Experimental evaluations conducted on hyperspectral image inpainting, multi-temporal image cloud removal, and hyperspectral image denoising have revealed the HNN's potential.
• Score: 53.68392692185276
• License:
• Abstract: Remote sensing image restoration aims to reconstruct missing or corrupted areas within images. To date, low-rank based models have garnered significant interest in this field. This paper proposes a novel low-rank regularization term, named the Haar nuclear norm (HNN), for efficient and effective remote sensing image restoration. It leverages the low-rank properties of wavelet coefficients derived from the 2-D frontal slice-wise Haar discrete wavelet transform, effectively modeling the low-rank prior for separated coarse-grained structure and fine-grained textures in the image. Experimental evaluations conducted on hyperspectral image inpainting, multi-temporal image cloud removal, and hyperspectral image denoising have revealed the HNN's potential. Typically, HNN achieves a performance improvement of 1-4 dB and a speedup of 10-28x compared to some state-of-the-art methods (e.g., tensor correlated total variation, and fully-connected tensor network) for inpainting tasks.

Related papers

• Enhanced Wavelet Scattering Network for image inpainting detection [0.0]
  This paper proposes several innovative ideas for detecting inpainting forgeries based on low level noise analysis. It combines Dual-Tree Complex Wavelet Transform (DT-CWT) for feature extraction with convolutional neural networks (CNN) for forged area detection and localization. Our approach was benchmarked against state-of-the-art methods and demonstrated superior performance over all cited alternatives.
  arXiv  Detail & Related papers  (2024-09-25T15:27:05Z)
• Multi-scale Restoration of Missing Data in Optical Time-series Images with Masked Spatial-Temporal Attention Network [0.6675733925327885]
  Existing methods for imputing missing values in remote sensing images fail to fully exploit auxiliary information. This paper proposes a deep learning-based novel approach called MS2 for reconstructing time-series remote sensing images.
  arXiv  Detail & Related papers  (2024-06-19T09:05:05Z)
• HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models [38.74983301496911]
  Hyperspectral image (HSI) restoration aims at recovering clean images from degraded observations. Existing model-based methods have limitations in accurately modeling the complex image characteristics. This paper proposes an unsupervised HSI restoration framework with pre-trained diffusion model (HIR-Diff)
  arXiv  Detail & Related papers  (2024-02-24T17:15:05Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations [111.08941206369508]
  We propose Augmented NeRF (Aug-NeRF), which for the first time brings the power of robust data augmentations into regularizing the NeRF training. Our proposal learns to seamlessly blend worst-case perturbations into three distinct levels of the NeRF pipeline. Aug-NeRF effectively boosts NeRF performance in both novel view synthesis and underlying geometry reconstruction.
  arXiv  Detail & Related papers  (2022-07-04T02:27:07Z)
• Frequency Consistent Adaptation for Real World Super Resolution [64.91914552787668]
  We propose a novel Frequency Consistent Adaptation (FCA) that ensures the frequency domain consistency when applying Super-Resolution (SR) methods to the real scene. We estimate degradation kernels from unsupervised images and generate the corresponding Low-Resolution (LR) images. Based on the domain-consistent LR-HR pairs, we train easy-implemented Convolutional Neural Network (CNN) SR models.
  arXiv  Detail & Related papers  (2020-12-18T08:25:39Z)
• 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)
• Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning [62.52242684874278]
  Cross-modality distribution of spatial and spectral information makes the problem challenging. We propose a novel textitlightweight deep neural network-based framework, namely PZRes-Net. Our framework learns a high resolution and textitzero-centric residual image, which contains high-frequency spatial details of the scene.
  arXiv  Detail & Related papers  (2020-06-18T06:32:11Z)
• Momentum-Net for Low-Dose CT Image Reconstruction [13.084578404699172]
  This paper applies the recent fast iterative neural network framework, Momentum-Net, using appropriate models to low-dose X-ray computed tomography (LDCT) image reconstruction. We show that the proposed Momentum-Net architecture significantly improves image reconstruction accuracy, compared to a state-of-the-art noniterative image denoising deep neural network (NN), WavResNet (in LDCT) We also investigated the spectral normalization technique that applies to image refining NN learning to satisfy the nonexpansive NN property; results show that this does not improve the image reconstruction performance of Momentum-Net
  arXiv  Detail & Related papers  (2020-02-27T10:26:22Z)

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.