Dual-Scale Transformer for Large-Scale Single-Pixel Imaging

• URL: http://arxiv.org/abs/2404.05001v1
• Date: Sun, 7 Apr 2024 15:53:21 GMT
• Title: Dual-Scale Transformer for Large-Scale Single-Pixel Imaging
• Authors: Gang Qu, Ping Wang, Xin Yuan,
• Abstract summary: We propose a deep unfolding network with hybrid-attention Transformer on Kronecker SPI model, dubbed HATNet, to improve the imaging quality of real SPI cameras. The gradient descent module can avoid high computational overheads rooted in previous gradient descent modules based on vectorized SPI. The denoising module is an encoder-decoder architecture powered by dual-scale spatial attention for high- and low-frequency aggregation and channel attention for global information recalibration.
• Score: 11.064806978728457
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Single-pixel imaging (SPI) is a potential computational imaging technique which produces image by solving an illposed reconstruction problem from few measurements captured by a single-pixel detector. Deep learning has achieved impressive success on SPI reconstruction. However, previous poor reconstruction performance and impractical imaging model limit its real-world applications. In this paper, we propose a deep unfolding network with hybrid-attention Transformer on Kronecker SPI model, dubbed HATNet, to improve the imaging quality of real SPI cameras. Specifically, we unfold the computation graph of the iterative shrinkagethresholding algorithm (ISTA) into two alternative modules: efficient tensor gradient descent and hybrid-attention multiscale denoising. By virtue of Kronecker SPI, the gradient descent module can avoid high computational overheads rooted in previous gradient descent modules based on vectorized SPI. The denoising module is an encoder-decoder architecture powered by dual-scale spatial attention for high- and low-frequency aggregation and channel attention for global information recalibration. Moreover, we build a SPI prototype to verify the effectiveness of the proposed method. Extensive experiments on synthetic and real data demonstrate that our method achieves the state-of-the-art performance. The source code and pre-trained models are available at https://github.com/Gang-Qu/HATNet-SPI.

Related papers

• DemosaicFormer: Coarse-to-Fine Demosaicing Network for HybridEVS Camera [70.28702677370879]
  Hybrid Event-Based Vision Sensor (HybridEVS) is a novel sensor integrating traditional frame-based and event-based sensors. Despite its potential, the lack of Image signal processing (ISP) pipeline specifically designed for HybridEVS poses a significant challenge. We propose a coarse-to-fine framework named DemosaicFormer which comprises coarse demosaicing and pixel correction.
  arXiv  Detail & Related papers  (2024-06-12T07:20:46Z)
• HAT: Hybrid Attention Transformer for Image Restoration [61.74223315807691]
  Transformer-based methods have shown impressive performance in image restoration tasks, such as image super-resolution and denoising. We propose a new Hybrid Attention Transformer (HAT) to activate more input pixels for better restoration. Our HAT achieves state-of-the-art performance both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2023-09-11T05:17:55Z)
• Improving Pixel-based MIM by Reducing Wasted Modeling Capability [77.99468514275185]
  We propose a new method that explicitly utilizes low-level features from shallow layers to aid pixel reconstruction. To the best of our knowledge, we are the first to systematically investigate multi-level feature fusion for isotropic architectures. Our method yields significant performance gains, such as 1.2% on fine-tuning, 2.8% on linear probing, and 2.6% on semantic segmentation.
  arXiv  Detail & Related papers  (2023-08-01T03:44:56Z)
• Deep Multi-Threshold Spiking-UNet for Image Processing [51.88730892920031]
  This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. Experimental results show that, on image segmentation and denoising, our Spiking-UNet achieves comparable performance to its non-spiking counterpart.
  arXiv  Detail & Related papers  (2023-07-20T16:00:19Z)
• Unfolding Framework with Prior of Convolution-Transformer Mixture and Uncertainty Estimation for Video Snapshot Compressive Imaging [7.601695814245209]
  We consider the problem of video snapshot compressive imaging (SCI), where sequential high-speed frames are modulated by different masks and captured by a single measurement. By combining optimization algorithms and neural networks, deep unfolding networks (DUNs) score tremendous achievements in solving inverse problems.
  arXiv  Detail & Related papers  (2023-06-20T06:25:48Z)
• Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method [51.30748775681917]
  We consider the task of low-light image enhancement (LLIE) and introduce a large-scale database consisting of images at 4K and 8K resolution. We conduct systematic benchmarking studies and provide a comparison of current LLIE algorithms. As a second contribution, we introduce LLFormer, a transformer-based low-light enhancement method.
  arXiv  Detail & Related papers  (2022-12-22T09:05:07Z)
• A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging [5.489791364472879]
  We propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features. We add two 2D processing modules, both convolutional encoder-decoder structures, to enhance multi-dimensional features of the imaging scene effectively. Compared with the conventional CS-based FISTA method and DL-based gamma-Net method, the result of our proposed method has better performance on completeness while having decent imaging accuracy.
  arXiv  Detail & Related papers  (2022-11-28T02:01:43Z)
• Solving combinational optimization problems with evolutionary single-pixel imaging [4.363127731705663]
  Single-pixel imaging (SPI) is a novel optical imaging technique by replacing the pixelated sensor array in a conventional camera with a single-pixel detector. In this work, we propose a SPI scheme for processing other types of data in addition to images.
  arXiv  Detail & Related papers  (2022-10-12T05:06:31Z)
• Effective Invertible Arbitrary Image Rescaling [77.46732646918936]
  Invertible Neural Networks (INN) are able to increase upscaling accuracy significantly by optimizing the downscaling and upscaling cycle jointly. A simple and effective invertible arbitrary rescaling network (IARN) is proposed to achieve arbitrary image rescaling by training only one model in this work. It is shown to achieve a state-of-the-art (SOTA) performance in bidirectional arbitrary rescaling without compromising perceptual quality in LR outputs.
  arXiv  Detail & Related papers  (2022-09-26T22:22:30Z)
• Deep-learned orthogonal basis patterns for fast, noise-robust single-pixel imaging [0.0]
  Single-pixel imaging (SPI) is a novel, unconventional method that goes beyond the notion of traditional cameras. Deep learning has been proposed as an alternative approach for solving the SPI reconstruction problem. We present a modified deep convolutional autoencoder network (DCAN) for SPI on 64x64 pixel images with up to 6.25% compression ratio.
  arXiv  Detail & Related papers  (2022-05-18T06:12:33Z)
• SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network [6.722629246312285]
  We propose a generative adversarial network-based reconstruction framework for single-pixel imaging, referred to as SPI-GAN. Our method can reconstruct images with 17.92 dB PSNR and 0.487 SSIM, even if the sampling ratio drops to 5%.
  arXiv  Detail & Related papers  (2021-07-03T03:06:09Z)

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.