Efficient and Robust Remote Sensing Image Denoising Using Randomized Approximation of Geodesics' Gramian on the Manifold Underlying the Patch Space

• URL: http://arxiv.org/abs/2504.10820v1
• Date: Tue, 15 Apr 2025 02:46:05 GMT
• Title: Efficient and Robust Remote Sensing Image Denoising Using Randomized Approximation of Geodesics' Gramian on the Manifold Underlying the Patch Space
• Authors: Kelum Gajamannage, Dilhani I. Jayathilake, Maria Vasilyeva,
• Abstract summary: We present a robust remote sensing image denoising method that doesn't require additional training samples.<n>The method asserts a unique emphasis on each color channel during denoising so the three denoised channels are merged to produce the final image.
• Score: 2.56711111236449
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Remote sensing images are widely utilized in many disciplines such as feature recognition and scene semantic segmentation. However, due to environmental factors and the issues of the imaging system, the image quality is often degraded which may impair subsequent visual tasks. Even though denoising remote sensing images plays an essential role before applications, the current denoising algorithms fail to attain optimum performance since these images possess complex features in the texture. Denoising frameworks based on artificial neural networks have shown better performance; however, they require exhaustive training with heterogeneous samples that extensively consume resources like power, memory, computation, and latency. Thus, here we present a computationally efficient and robust remote sensing image denoising method that doesn't require additional training samples. This method partitions patches of a remote-sensing image in which a low-rank manifold, representing the noise-free version of the image, underlies the patch space. An efficient and robust approach to revealing this manifold is a randomized approximation of the singular value spectrum of the geodesics' Gramian matrix of the patch space. The method asserts a unique emphasis on each color channel during denoising so the three denoised channels are merged to produce the final image.

Related papers

• Linear Combinations of Patches are Unreasonably Effective for Single-Image Denoising [5.893124686141782]
  Deep neural networks have revolutionized image denoising in achieving significant accuracy improvements. To alleviate the requirement to learn image priors externally, single-image methods perform denoising solely based on the analysis of the input noisy image. This work investigates the effectiveness of linear combinations of patches for denoising under this constraint.
  arXiv  Detail & Related papers  (2022-12-01T10:52:03Z)
• Deep Unfolding for Iterative Stripe Noise Removal [4.756256077972335]
  Non-uniform photoelectric response of infrared imaging systems results in fixed-pattern stripe noise being superimposed on infrared images. Existing image destriping methods struggle to concurrently remove all stripe noise artifacts, preserve image details and structures, and balance real-time performance. We propose a novel algorithm for destriping degraded images, which takes advantage of neighbouring column signal correlation to remove independent column stripe noise.
  arXiv  Detail & Related papers  (2022-09-27T02:53:03Z)
• Deep Semantic Statistics Matching (D2SM) Denoising Network [70.01091467628068]
  We introduce the Deep Semantic Statistics Matching (D2SM) Denoising Network. It exploits semantic features of pretrained classification networks, then it implicitly matches the probabilistic distribution of clear images at the semantic feature space. By learning to preserve the semantic distribution of denoised images, we empirically find our method significantly improves the denoising capabilities of networks.
  arXiv  Detail & Related papers  (2022-07-19T14:35:42Z)
• Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training [50.018580462619425]
  We propose a novel framework, namely Pixel-level Noise-aware Generative Adrial Network (PNGAN) PNGAN employs a pre-trained real denoiser to map the fake and real noisy images into a nearly noise-free solution space. For better noise fitting, we present an efficient architecture Simple Multi-versa-scale Network (SMNet) as the generator.
  arXiv  Detail & Related papers  (2022-04-06T14:09:02Z)
• A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory [40.29747436872773]
  This paper presents a novel image denoising algorithm inspired by the quantum many-body theory. Based on patch analysis, the similarity measures in a local image neighborhood are formalized through a term akin to interaction in quantum mechanics. We show the ability of our approach to deal with practical images denoising problems such as medical ultrasound image despeckling applications.
  arXiv  Detail & Related papers  (2021-12-16T23:34:37Z)
• Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting [102.19116213923614]
  We propose a boosting network to perform denoising and segmentation alternately. We observe that not only denoising helps combat the drop of segmentation accuracy due to noise, but also pixel-wise semantic information boosts the capability of denoising. Experimental results show that the denoised image quality is improved substantially and the segmentation accuracy is improved to close to that of clean images.
  arXiv  Detail & Related papers  (2021-02-24T06:48:45Z)
• 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)
• Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images [98.82804259905478]
  We present Neighbor2Neighbor to train an effective image denoising model with only noisy images. In detail, input and target used to train a network are images sub-sampled from the same noisy image. A denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance.
  arXiv  Detail & Related papers  (2021-01-08T02:03:25Z)
• Image Denoising Using the Geodesics' Gramian of the Manifold Underlying Patch-Space [1.7767466724342067]
  We propose a novel and computationally efficient image denoising method that is capable of producing accurate images. To preserve image smoothness, this method inputs patches partitioned from the image rather than pixels. We validate the performance of this method against benchmark image processing methods.
  arXiv  Detail & Related papers  (2020-10-14T04:07:24Z)
• Reconstructing the Noise Manifold for Image Denoising [56.562855317536396]
  We introduce the idea of a cGAN which explicitly leverages structure in the image noise space. By learning directly a low dimensional manifold of the image noise, the generator promotes the removal from the noisy image only that information which spans this manifold. Based on our experiments, our model substantially outperforms existing state-of-the-art architectures.
  arXiv  Detail & Related papers  (2020-02-11T00:31:31Z)
• Spatial-Adaptive Network for Single Image Denoising [14.643663950015334]
  We propose a novel spatial-adaptive denoising network (SADNet) for efficient single image blind noise removal. Our method can surpass the state-of-the-art denoising methods both quantitatively and visually.
  arXiv  Detail & Related papers  (2020-01-28T12:24:17Z)

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.