MTS-CSNet: 多尺度张量分解用于RGB图像的深度压缩感知
MTS-CSNet: Multiscale Tensor Factorization for Deep Compressive Sensing on RGB Images
AI总结：
MTS-CSNet通过多尺度张量分解实现高效RGB图像压缩感知，提供更优的重建性能和更快的推理速度。
摘要：
基于深度学习的压缩感知（CS）方法通常通过卷积或分块全连接层学习采样算子，这限制了感受野并使高维数据处理效果不佳。我们提出了MTSCSNet，一种基于多尺度张量求和（MTS）分解的CS框架，这是一种结构化的算子，用于高效多维信号处理。MTS通过多尺度求和执行模式级线性变换，使感受野增大并有效建模跨维度相关性。在MTSCSNet中，MTS首先用作可学习的CS算子，在张量空间中执行线性降维，其共轭定义了初始反投影，然后在重建阶段直接细化此估计。这导致了一个简单的前馈架构，无需迭代或近端优化，同时保持参数和计算效率。在标准CS基准测试中，MTSCSNet在RGB图像上实现了最先进的重建性能，具有显著的PSNR增益和更快的推理速度，即使与最近的扩散基CS方法相比，其使用的前馈架构也显著更紧凑。
英文摘要：
Deep learning based compressive sensing (CS) methods typically learn sampling operators using convolutional or block wise fully connected layers, which limit receptive fields and scale poorly for high dimensional data. We propose MTSCSNet, a CS framework based on Multiscale Tensor Summation (MTS) factorization, a structured operator for efficient multidimensional signal processing. MTS performs mode-wise linear transformations with multiscale summation, enabling large receptive fields and effective modeling of cross-dimensional correlations. In MTSCSNet, MTS is first used as a learnable CS operator that performs linear dimensionality reduction in tensor space, with its adjoint defining the initial back-projection, and is then applied in the reconstruction stage to directly refine this estimate. This results in a simple feed-forward architecture without iterative or proximal optimization, while remaining parameter and computation efficient. Experiments on standard CS benchmarks show that MTSCSNet achieves state-of-the-art reconstruction performance on RGB images, with notable PSNR gains and faster inference, even compared to recent diffusion-based CS methods, while using a significantly more compact feed-forward architecture.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
