WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing

• URL: http://arxiv.org/abs/2411.16336v1
• Date: Mon, 25 Nov 2024 12:31:03 GMT
• Title: WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing
• Authors: Kai Han, Jin Wang, Yunhui Shi, Hanqin Cai, Nam Ling, Baocai Yin,
• Abstract summary: We propose a novel wavelet-domain deep unfolding framework named WTDUN, which operates directly on the multi-scale wavelet subbands. Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction.
• Score: 51.94493817128006
• License:
• Abstract: Deep unfolding networks have gained increasing attention in the field of compressed sensing (CS) owing to their theoretical interpretability and superior reconstruction performance. However, most existing deep unfolding methods often face the following issues: 1) they learn directly from single-channel images, leading to a simple feature representation that does not fully capture complex features; and 2) they treat various image components uniformly, ignoring the characteristics of different components. To address these issues, we propose a novel wavelet-domain deep unfolding framework named WTDUN, which operates directly on the multi-scale wavelet subbands. Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction, effectively capturing and highlighting the most important features within images. Specifically, the design of tree-structured reconstruction aims to capture the inter-dependencies among the multi-scale subbands, enabling the identification of both fine and coarse features, which can lead to a marked improvement in reconstruction quality. Furthermore, a wavelet domain adaptive sampling method is proposed to greatly improve the sampling capability, which is realized by assigning measurements to each wavelet subband based on its importance. Unlike pure deep learning methods that treat all components uniformly, our method introduces a targeted focus on important subbands, considering their energy and sparsity. This targeted strategy lets us capture key information more efficiently while discarding less important information, resulting in a more effective and detailed reconstruction. Extensive experimental results on various datasets validate the superior performance of our proposed method.

Related papers

• Wavelet-based Bi-dimensional Aggregation Network for SAR Image Change Detection [53.842568573251214]
  Experimental results on three SAR datasets demonstrate that our WBANet significantly outperforms contemporary state-of-the-art methods. Our WBANet achieves 98.33%, 96.65%, and 96.62% of percentage of correct classification (PCC) on the respective datasets.
  arXiv  Detail & Related papers  (2024-07-18T04:36:10Z)
• A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling [54.05517338122698]
  We propose an explicitly controllable query-key feature alignment from both semantic-aware and detail-aware perspectives. We also develop a fine-grained neighbor selection strategy on HR features, which is simple yet effective for alleviating mosaic artifacts. Our proposed ReSFU framework consistently achieves satisfactory performance on different segmentation applications.
  arXiv  Detail & Related papers  (2024-07-02T14:12:21Z)
• Hybrid Feature Collaborative Reconstruction Network for Few-Shot Fine-Grained Image Classification [6.090855292102877]
  We design a new Hybrid Feature Collaborative Reconstruction Network (HFCR-Net) for few-shot fine-grained image classification. We fuse the channel features and the spatial features to increase the inter-class differences. Our experiments on three widely used fine-grained datasets demonstrate the effectiveness and superiority of our approach.
  arXiv  Detail & Related papers  (2024-07-02T10:14:00Z)
• Learning Image Deraining Transformer Network with Dynamic Dual Self-Attention [46.11162082219387]
  This paper proposes an effective image deraining Transformer with dynamic dual self-attention (DDSA) Specifically, we only select the most useful similarity values based on top-k approximate calculation to achieve sparse attention. In addition, we also develop a novel spatial-enhanced feed-forward network (SEFN) to further obtain a more accurate representation for achieving high-quality derained results.
  arXiv  Detail & Related papers  (2023-08-15T13:59:47Z)
• Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising [50.039949798156826]
  This paper tackles the challenging problem of hyperspectral (HS) image denoising. We propose rank-enhanced low-dimensional convolution set (Re-ConvSet) We then incorporate Re-ConvSet into the widely-used U-Net architecture to construct an HS image denoising method.
  arXiv  Detail & Related papers  (2022-07-09T13:35:12Z)
• i-SpaSP: Structured Neural Pruning via Sparse Signal Recovery [11.119895959906085]
  We propose a novel, structured pruning algorithm for neural networks -- the iterative, Sparse Structured Pruning, dubbed as i-SpaSP. i-SpaSP operates by identifying a larger set of important parameter groups within a network that contribute most to the residual between pruned and dense network output. It is shown to discover high-performing sub-networks and improve upon the pruning efficiency of provable baseline methodologies by several orders of magnitude.
  arXiv  Detail & Related papers  (2021-12-07T05:26:45Z)
• Feedback Pyramid Attention Networks for Single Image Super-Resolution [37.58180059860872]
  We propose feedback pyramid attention networks (FPAN) to fully exploit the mutual dependencies of features. In our method, the output of each layer in the first stage is also used as the input of the corresponding layer in the next state to re-update the previous low-level filters. We introduce a pyramid non-local structure to model global contextual information in different scales and improve the discriminative representation of the network.
  arXiv  Detail & Related papers  (2021-06-13T11:32:53Z)
• High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion [84.24973877109181]
  We propose a novel attention-based hierarchical multi-modal fusion network for guided DSR. We show that our approach outperforms state-of-the-art methods in terms of reconstruction accuracy, running speed and memory efficiency.
  arXiv  Detail & Related papers  (2021-04-04T03:28:33Z)
• Image super-resolution reconstruction based on attention mechanism and feature fusion [3.42658286826597]
  A network structure based on attention mechanism and multi-scale feature fusion is proposed. Experimental results show that the proposed method can achieve better performance over other representative super-resolution reconstruction algorithms.
  arXiv  Detail & Related papers  (2020-04-08T11:20:10Z)
• ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising [52.01041506447195]
  We propose an attention-based deep residual network to learn a mapping from noisy HSI to the clean one. Experimental results demonstrate that our proposed ADRN scheme outperforms the state-of-the-art methods both in quantitative and visual evaluations.
  arXiv  Detail & Related papers  (2020-03-04T08:36:27Z)

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.