Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration

• URL: http://arxiv.org/abs/2602.21917v1
• Date: Wed, 25 Feb 2026 13:45:50 GMT
• Title: Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration
• Authors: Chen Wu, Ling Wang, Zhuoran Zheng, Yuning Cui, Zhixiong Yang, Xiangyu Chen, Yue Zhang, Weidong Jiang, Jingyuan Xia,
• Abstract summary: C$2$SSM is a visual state space model that shifts from pixel-serial to cluster-serial scanning.<n>Our core discovery is that the rich feature distribution of a UHD image can be distilled into a sparse set of semantic centroids.<n>More than a solution, C$2$SSM charts a new course for efficient large-scale vision: scan clusters, not pixels.
• Score: 28.17529244607509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ultra-High-Definition (UHD) image restoration is trapped in a scalability crisis: existing models, bound to pixel-wise operations, demand unsustainable computation. While state space models (SSMs) like Mamba promise linear complexity, their pixel-serial scanning remains a fundamental bottleneck for the millions of pixels in UHD content. We ask: must we process every pixel to understand the image? This paper introduces C$^2$SSM, a visual state space model that breaks this taboo by shifting from pixel-serial to cluster-serial scanning. Our core discovery is that the rich feature distribution of a UHD image can be distilled into a sparse set of semantic centroids via a neural-parameterized mixture model. C$^2$SSM leverages this to reformulate global modeling into a novel dual-path process: it scans and reasons over a handful of cluster centers, then diffuses the global context back to all pixels through a principled similarity distribution, all while a lightweight modulator preserves fine details. This cluster-centric paradigm achieves a decisive leap in efficiency, slashing computational costs while establishing new state-of-the-art results across five UHD restoration tasks. More than a solution, C$^2$SSM charts a new course for efficient large-scale vision: scan clusters, not pixels.

Related papers

• Joint Superpixel and Self-Representation Learning for Scalable Hyperspectral Image Clustering [42.598744707088535]
  Subspace clustering is a powerful unsupervised approach for hyperspectral image (HSI) analysis.<n>Superpixel segmentation can improve efficiency by reducing the number of data points to process.<n>We propose a unified end-to-end framework that jointly optimize superpixel segmentation and subspace clustering.
  arXiv  Detail & Related papers  (2025-09-28T18:42:48Z)
• Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering [73.01141916544103]
  Hyperspectral image (HSI) clustering groups pixels into clusters without labeled data.<n>Most methods rely on superpixel segmentation and perform superpixel-level clustering based on graph neural networks (GNNs)<n>We propose a structural-spectral graph convolutional operator (SSGCO) tailored for graph-structured HSI superpixels to improve representation quality.
  arXiv  Detail & Related papers  (2025-06-11T16:41:34Z)
• Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling [50.34513854725803]
  Arbitrary-scale super-resolution (ASSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs with arbitrary upsampling factors.<n>We propose a novel ContinuousSR framework with a Pixel-to-Gaussian paradigm, which explicitly reconstructs 2D continuous HR signals from LR images using Gaussian Splatting.
  arXiv  Detail & Related papers  (2025-03-09T13:43:57Z)
• HAIR: Hypernetworks-based All-in-One Image Restoration [46.681872835394095]
  Hair is a Hypernetworks-based All-in-One Image Restoration plug-and-play method. It generates parameters based on the input image and thus makes the model to adapt to specific degradation dynamically. It can significantly improve the performance of existing image restoration models in a plug-and-play manner, both in single-task and All-in-One settings.
  arXiv  Detail & Related papers  (2024-08-15T11:34:33Z)
• Accelerating Image Super-Resolution Networks with Pixel-Level Classification [29.010136088811137]
  Pixel-level for Single Image SuperResolution is a novel method designed to distribute computational resources adaptively at the pixel level. Our method allows for performance and computational cost balance during inference without re-training.
  arXiv  Detail & Related papers  (2024-07-31T08:53:10Z)
• Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images [73.01141916544103]
  Hyperspectral images (HSI) clustering is an important but challenging task.<n>We first use 3-D and 2-D hybrid convolutional neural networks to extract the high-order spatial and spectral features of HSI.<n>We then design a superpixel graph contrastive clustering model to learn discriminative superpixel representations.
  arXiv  Detail & Related papers  (2024-03-04T07:40:55Z)
• Class Balanced PixelNet for Neurological Image Segmentation [20.56747443955369]
  We propose an automatic brain tumor segmentation approach (e.g., PixelNet) using a pixel-level convolutional neural network (CNN) The proposed model has achieved promising results in brain tumor and ischemic stroke segmentation datasets.
  arXiv  Detail & Related papers  (2022-04-23T10:57:54Z)
• Learning Enriched Features for Fast Image Restoration and Enhancement [166.17296369600774]
  This paper presents a holistic goal of maintaining spatially-precise high-resolution representations through the entire network. We learn an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Our approach achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.
  arXiv  Detail & Related papers  (2022-04-19T17:59:45Z)
• UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution [74.82282301089994]
  In this work, we propose UltraSR, a simple yet effective new network design based on implicit image functions. We show that spatial encoding is indeed a missing key towards the next-stage high-accuracy implicit image function. Our UltraSR sets new state-of-the-art performance on the DIV2K benchmark under all super-resolution scales.
  arXiv  Detail & Related papers  (2021-03-23T17:36:42Z)
• Implicit Integration of Superpixel Segmentation into Fully Convolutional Networks [11.696069523681178]
  We propose a way to implicitly integrate a superpixel scheme into CNNs. Our proposed method hierarchically groups pixels at downsampling layers and generates superpixels. We evaluate our method on several tasks such as semantic segmentation, superpixel segmentation, and monocular depth estimation.
  arXiv  Detail & Related papers  (2021-03-05T02:20:26Z)
• Locally Masked Convolution for Autoregressive Models [107.4635841204146]
  LMConv is a simple modification to the standard 2D convolution that allows arbitrary masks to be applied to the weights at each location in the image. We learn an ensemble of distribution estimators that share parameters but differ in generation order, achieving improved performance on whole-image density estimation.
  arXiv  Detail & Related papers  (2020-06-22T17:59:07Z)
• 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)

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.