Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning
This work addresses the challenge of implementing deep learning in practical SPI setups, potentially improving image reconstruction for applications like low-light or specialized imaging, though it is incremental as it builds on existing CS and deep learning techniques.
The paper tackled the problem of reconstructing images from single-pixel imaging (SPI) by combining block compressive sensing (BCS) with a convolutional neural network model, achieving superior performance over other compressive sensing algorithms and enabling reconstruction of images of varying sizes and from different domains.
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.