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

arXiv: https://arxiv.org/abs/1908.11221 · HTML: https://mlsanity.com/paper/1908.11221/

## Key facts

- Cited by 22 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 22 of 22)

- [Memory-Augmented Deep Unfolding Network for Compressive Sensing](https://mlsanity.com/paper/2110.09766/) (2021)
- [Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems](https://mlsanity.com/paper/2112.10948/) (2021)
- [Content-aware Scalable Deep Compressed Sensing](https://mlsanity.com/paper/2207.09313/) (2022)
- [Image Compressed Sensing Using Non-local Neural Network](https://mlsanity.com/paper/2112.03712/) (2021)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing](https://mlsanity.com/paper/2208.01827/) (2022)
- [Deep Unfolding Network for Image Compressed Sensing by Content-adaptive Gradient Updating and Deformation-invariant Non-local Modeling](https://mlsanity.com/paper/2310.10033/) (2023)
- [Dynamic Proximal Unrolling Network for Compressive Imaging](https://mlsanity.com/paper/2107.11007/) (2021)
- [Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation](https://mlsanity.com/paper/2207.08547/) (2022)
- [Robust Deep Compressive Sensing with Recurrent-Residual Structural Constraints](https://mlsanity.com/paper/2207.07301/) (2022)
- [Uformer-ICS: A U-Shaped Transformer for Image Compressive Sensing Service](https://mlsanity.com/paper/2209.01763/) (2022)
- [MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network](https://mlsanity.com/paper/2402.16855/) (2024)
- [Sampling Innovation-Based Adaptive Compressive Sensing](https://mlsanity.com/paper/2503.13241/) (2025)
- [Recovering Sign Bits of DCT Coefficients in Digital Images as an Optimization Problem](https://mlsanity.com/paper/2211.01096/) (2022)
- [Sparsity and Coefficient Permutation Based Two-Domain AMP for Image Block Compressed Sensing](https://mlsanity.com/paper/2305.12986/) (2023)
- [Image Quality Assessment With Compressed Sampling](https://mlsanity.com/paper/2404.17170/) (2024)
- [Block based Adaptive Compressive Sensing with Sampling Rate Control](https://mlsanity.com/paper/2411.10200/) (2024)
- [WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing](https://mlsanity.com/paper/2411.16336/) (2024)
- [Block-Based Multi-Scale Image Rescaling](https://mlsanity.com/paper/2412.11468/) (2024)
- [Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization](https://mlsanity.com/paper/2505.11518/) (2025)
- [Dual-Path Hyperprior Informed Deep Unfolding Network for Image Compressive Sensing](https://mlsanity.com/paper/2605.09566/) (2026)
- [Beyond Single Solution: Multi-Hypothesis Collaborative Deep Unfolding Network for Image Compressive Sensing](https://mlsanity.com/paper/2606.03666/) (2026)

## Abstract (arXiv)

Incorporating deep neural networks in image compressive sensing (CS) receives intensive attentions in multimedia technology and applications recently. As deep network approaches learn the inverse mapping directly from the CS measurements, the reconstruction speed is significantly faster than the conventional CS algorithms. However, for existing network based approaches, a CS sampling procedure has to map a separate network model. This may potentially degrade the performance of image CS with block-wise sampling because of blocking artifacts, especially when multiple sampling rates are assigned to different blocks within an image. In this paper, we develop a multichannel deep network for block-based image CS by exploiting inter-block correlation with performance significantly exceeding the current state-of-the-art methods. The significant performance improvement is attributed to block-wise approximation but full image removal of blocking artifacts. Specifically, with our multichannel structure, the image blocks with a variety of sampling rates can be reconstructed in a single model. The initially reconstructed blocks are then capable of being reassembled into a full image to improve the recovered images by unrolling a hand-designed block based CS recovery 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 and subjective visual image quality. Our source codes are available at https://github.com/siwangzhou/DeepBCS.