Deep learning (DL) models are highly research-oriented field in image compressive sensing in the recent studies. In compressive sensing theory, a signal is efficiently reconstructed from very small and limited number of measurements. Block-based compressive sensing is most promising and lenient compressive sensing (CS) approach mostly used to process large-sized videos and images: exploit low computational complexity and requires less memory. In block-based compressive sensing, a number of deep models are needed to train with each corresponding to different sampling rate. Compressive sensing performance is highly degraded through allocating low sampling rates to various blocks within same image or video frames. In this work, we proposed multi-rate method using deep neural networks for block-based compressive sensing of magnetic resonance images with performance that greatly outperforms existing state-of-the-art methods. The proposed approach is capable in smart allocation of exclusive sampling rate for each block within image, based on the image information and removing blocking artifacts in reconstructed MRI images. Each image block is separately sampled and reconstructed with different sampling rate and reassembled into a single image based on inter-correlation between blocks, to remove blocking artifacts. The proposed method surpasses the current state-of-the-arts in terms of reconstruction speed, reconstruction error, low computational complexity, and certain evaluation metrics such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and relative l₂-norm error (RLNE).

在最近的研究中，深度学习 (DL) 模型是图像压缩感知中高度研究导向的领域。在压缩传感理论中，信号可以从非常少且数量有限的测量中有效地重建。基于块的压缩感知是最有前途和宽松的压缩感知 (CS) 方法，主要用于处理大型视频和图像：利用低计算复杂度和需要更少的内存。在基于块的压缩感知中，需要许多深度模型来训练，每个模型对应于不同的采样率。通过将低采样率分配给同一图像或视频帧内的各个块，压缩感知性能会大大降低。在这项工作中，我们提出了使用深度神经网络对磁共振图像进行基于块的压缩感知的多速率方法，其性能大大优于现有的最先进方法。所提出的方法能够根据图像信息为图像中的每个块智能分配独占采样率，并去除重建的 MRI 图像中的块伪影。每个图像块分别以不同的采样率进行采样和重建，并根据块之间的相关性重新组合成单​​个图像，以消除块伪影。所提出的方法在重建速度、重建误差、低计算复杂度以及某些评估指标（如峰值信噪比（PSNR）、结构相似度（SSIM）、l _{2 -}范数错误（RLNE）。