Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning

• URL: http://arxiv.org/abs/2207.06746v1
• Date: Thu, 14 Jul 2022 08:55:41 GMT
• Title: Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning
• Authors: Stephen L. H. Lau and Edwin K. P. Chong
• Abstract summary: Single-pixel imaging (SPI) is a novel imaging technique whose working principle is based on the compressive sensing theory. Recent advances in deep learning have found its uses in reconstructing CS images. We show that our model is capable of reconstructing images obtained from an SPI setup while being priorly trained on natural images.
• Score: 0.40611352512781856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single-pixel imaging (SPI) is a novel imaging technique whose working principle is based on the compressive sensing (CS) theory. In SPI, data is obtained through a series of compressive measurements and the corresponding image is reconstructed. Typically, the reconstruction algorithm such as basis pursuit relies on the sparsity assumption in images. However, recent advances in deep learning have found its uses in reconstructing CS images. Despite showing a promising result in simulations, it is often unclear how such an algorithm can be implemented in an actual SPI setup. In this paper, we demonstrate the use of deep learning on the reconstruction of SPI images in conjunction with block compressive sensing (BCS). We also proposed a novel reconstruction model based on convolutional neural networks that outperforms other competitive CS reconstruction algorithms. Besides, by incorporating BCS in our deep learning model, we were able to reconstruct images of any size above a certain smallest image size. In addition, we show that our model is capable of reconstructing images obtained from an SPI setup while being priorly trained on natural images, which can be vastly different from the SPI images. This opens up opportunity for the feasibility of pretrained deep learning models for CS reconstructions of images from various domain areas.

Related papers

• Dual-Scale Transformer for Large-Scale Single-Pixel Imaging [11.064806978728457]
  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.
  arXiv  Detail & Related papers  (2024-04-07T15:53:21Z)
• Uformer-ICS: A U-Shaped Transformer for Image Compressive Sensing Service [43.23436916495013]
  We propose Uformer-ICS as a novel U-shaped transformer for image CS tasks. We design an adaptive sampling architecture that allocates measurement resources based on the estimated block sparsity. Our reconstruction model can simultaneously utilize the local features and long-range dependencies of image.
  arXiv  Detail & Related papers  (2022-09-05T04:52:12Z)
• 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)
• 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)
• Is Deep Image Prior in Need of a Good Education? [57.3399060347311]
  Deep image prior was introduced as an effective prior for image reconstruction. Despite its impressive reconstructive properties, the approach is slow when compared to learned or traditional reconstruction techniques. We develop a two-stage learning paradigm to address the computational challenge.
  arXiv  Detail & Related papers  (2021-11-23T15:08:26Z)
• 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)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging [48.34565372026196]
  We propose a novel HSI reconstruction method based on the a Posterior (MAP) estimation framework. We also propose to estimate the local means of the GSM models by the deep convolutional neural network (DCNN)
  arXiv  Detail & Related papers  (2021-03-12T08:57:06Z)
• NAS-DIP: Learning Deep Image Prior with Neural Architecture Search [65.79109790446257]
  Recent work has shown that the structure of deep convolutional neural networks can be used as a structured image prior. We propose to search for neural architectures that capture stronger image priors. We search for an improved network by leveraging an existing neural architecture search algorithm.
  arXiv  Detail & Related papers  (2020-08-26T17:59:36Z)
• Neural Sparse Representation for Image Restoration [116.72107034624344]
  Inspired by the robustness and efficiency of sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks. Our method structurally enforces sparsity constraints upon hidden neurons. Experiments show that sparse representation is crucial in deep neural networks for multiple image restoration tasks.
  arXiv  Detail & Related papers  (2020-06-08T05:15:17Z)

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.