Multi-Channel Deep Networks for Block-Based Image Compressive Sensing

--Incorporating deep neural networks in image com-pressive sensing (CS) receives intensive attentions recently. As deep network approaches learn the inverse mapping directly from the CS measurements, a number of models have to be trained, each of which corresponds to a sampling rate. This may potentially degrade the performance of image CS, especially when multiple sampling rates are assigned to different blocks within an image. In this paper, we develop a multi-channel deep network for block-based image CS with performance significantly exceeding the current state-of-the-art methods. The significant performance improvement of the model is attributed to block-based sampling rates allocation and model-level removal of blocking artifacts. Specifically, the image blocks with a variety of sampling rates can be reconstructed in a single model by exploiting inter-block correlation. At the same time, the initially reconstructed blocks are reassembled into a full image to remove blocking artifacts within the network by unrolling a hand-designed block-based CS algorithm. Experimental results demonstrate that the proposed method outperforms the state-of-the-art CS methods by a large margin in terms of objective metrics, PSNR, SSIM, and subjective visual quality. Compressive sensing (CS), an emerging sampling and reconstructing strategy, can recover original signal from dramatically fewer measurements with a sub-Nyquist sampling rate [1]. As CS has the potentials of significantly improving the sampling speed and sensor energy efficiency, it has been applied in many practical applications, including single pixel imaging [2], fast magnetic resonance imaging [3], high-speed video cameras [4] and image encryption [5]. To deal with high-dimensional natural images efficiently, block-based CS is proposed as a lightweight CS approach [6]-[8]. In such strategy, a scene under view is partitioned into some small blocks, which are then sampled and reconstructed independently. Meaningful information is usually not uniformly distributed in an image, so the block partition benefits more fair allocation of the sensing resources for the whole image [9]. This work was supported by the National Natural Science Foundation of China (no.