Recent years have witnessed the success of deep networks in compressed sensing (CS), which allows for a significant reduction in sampling cost and has gained growing attention since its inception. In this paper, we propose a new practical and compact network dubbed PCNet for general image CS. Specifically, in PCNet, a novel collaborative sampling operator is designed, which consists of a deep conditional filtering step and a dual-branch fast sampling step. The former learns an implicit representation of a linear transformation matrix into a few convolutions and first performs adaptive local filtering on the input image, while the latter then uses a discrete cosine transform and a scrambled block-diagonal Gaussian matrix to generate under-sampled measurements. Our PCNet is equipped with an enhanced proximal gradient descent algorithm-unrolled network for reconstruction. It offers flexibility, interpretability, and strong recovery performance for arbitrary sampling rates once trained. Additionally, we provide a deployment-oriented extraction scheme for single-pixel CS imaging systems, which allows for the convenient conversion of any linear sampling operator to its matrix form to be loaded onto hardware like digital micro-mirror devices. Extensive experiments on natural image CS, quantized CS, and self-supervised CS demonstrate the superior reconstruction accuracy and generalization ability of PCNet compared to existing state-of-the-art methods, particularly for high-resolution images. Code is available at https://github.com/Guaishou74851/PCNet.

翻译：近年来，深度网络在压缩感知领域取得了显著成功，其能够大幅降低采样成本，自提出以来受到日益广泛的关注。本文提出一种新型实用紧凑网络PCNet，用于通用图像压缩感知任务。具体而言，PCNet设计了一种创新的协同采样算子，该算子包含深度条件滤波步骤与双分支快速采样步骤。前者通过若干卷积层学习线性变换矩阵的隐式表示，并首先对输入图像进行自适应局部滤波处理；后者随后采用离散余弦变换与置乱块对角高斯矩阵生成欠采样测量值。我们的PCNet配备了增强型近端梯度下降算法展开网络用于重建，在完成训练后可为任意采样率提供灵活性、可解释性及强大的恢复性能。此外，我们为单像素压缩感知成像系统提供了面向部署的提取方案，该方案可将任意线性采样算子便捷地转换为矩阵形式，以便加载至数字微镜器件等硬件平台。在自然图像压缩感知、量化压缩感知及自监督压缩感知任务上的大量实验表明，相较于现有先进方法，PCNet具有更优的重建精度与泛化能力，尤其在高分辨率图像处理方面表现突出。代码已开源：https://github.com/Guaishou74851/PCNet。