Multi-view or stereo image compression is an essential technology in 3D related applications. Due to the overlap between different views, exploring their correlations can help improve the compression rate. However, the computing complexity of joint encoding at the encoding side is a heavy burden for terminal encoders. To solve this problem, the learned Distributed Image Coding (DIC), which only uses the correlated view (namely the side image, SI) in the decoder side, has gained much attention in recent years. In this work, we explore asymmetric DIC where one view is selected as the SI and is losslessly compressed. The key problem in learned asymmetric DIC is alignment between the transmitted low-quality target image and high-quality SI. Previous methods usually adopt patch-level alignment with the offset index obtained from degraded (via re-encoded and decoded) SI and the decoded target image, which hinders the alignment accuracy. In this work, we propose a dual domain alignment strategy, which includes degraded domain and fused domain pixel-wise offset estimation. For the degraded domain alignment, we estimate the offset between the degraded SI feature and the degraded target image feature, which eliminates the difficulties in cross-domain matching. For the fused-domain alignment, we observe that the fusion result of degraded target feature and aligned side image feature implicitly contains fine-scale disparity information. Therefore, we estimate the fine-scale offset from the fusion result, which helps refine the degraded domain offsets. We further propose a selective enhancement module to repair the mismatched region in the aligned feature. Extensive experiments on three datasets demonstrate the superiority of our proposed method, outperforming the second-best method by 16% in terms of average BD-rate reduction on the KITTI Stereo dataset. Our code is available at https://github.com/lixianghuitju/DIC-DDA.

The dense light field sampling of focused plenoptic images (FPIs) yields substantial amounts of redundant data, necessitating efficient compression in practical applications. However, the presence of discontinuous structures and long-distance properties in FPIs poses a challenge. In this paper, we propose a novel end-to-end approach for learned focused plenoptic image compression (LFPIC). Specifically, we introduce a local-global correlation learning strategy to build the nonlinear transforms. This strategy can effectively handle the discontinuous structures and leverage long-distance correlations in FPI for high compression efficiency. Additionally, we propose a spatial-wise context model tailored for LFPIC to help emphasize the most related symbols during coding and further enhance the rate-distortion performance. Experimental results demonstrate the effectiveness of our proposed method, achieving a 22.16% BD-rate reduction (measured in PSNR) on the public dataset compared to the recent state-of-the-art LFPIC method. This improvement holds significant promise for benefiting the applications of focused plenoptic cameras.

The vision of sustainable IoT constructed from battery-free devices has attracted ample interest in the research community. Yet, efficient device discovery and synchronization—a fundamental problem in IoT systems—remains a critical challenge mainly due to the uncertain ambient energy availability across battery-free devices. We argue that bringing in a small level of certainty is necessary for facilitating communication in battery-free IoT. We propose Pulsar where we introduce a small number of battery-powered devices, serving as the communication coordinator for a large number of battery-free devices. We develop two communication schemes, namely one-to-one, and all-to-all, for Pulsar. Our results based on simulations and prototype-based experiments show that Pulsar achieves consistently good performance across different scenarios while requiring no special hardware or environmental conditions.

The rising demand for sustainable IoT has promoted the adoption of battery-free devices intermittently powered by ambient energy for sensing. However, the intermittency poses significant challenges in sensing data collection. Despite recent efforts to enable one-to-one communication, routing data across multiple intermittently-powered battery-free devices, a crucial requirement for a sensing system, remains a formidable challenge. This paper fills this gap by introducing Swift, which enables seamless data routing in intermittently-powered battery-free sensing systems. Swift overcomes the challenges posed by device intermittency and heterogeneous energy conditions through three major innovative designs. First, Swift incorporates a reliable node synchronization protocol backed by number theory, ensuring successful synchronization regardless of energy conditions. Second, Swift adopts a low-latency message forwarding protocol, allowing continuous message forwarding without repeated synchronization. Finally, Swift features a simple yet effective mechanism for routing path construction, enabling nodes to obtain the optimal path to the sink node with minimum hops. We implement Swift and perform large-scale experiments representing diverse real-world scenarios. The results demonstrate that Swift achieves an order of magnitude reduction in end-to-end message delivery time compared with the state-of-the-art approaches for intermittently-powered battery-free sensing systems.

Recently, intermittent computing (IC) has received tremendous attention due to its high potential in perpetual sensing for Internet-of-Things (IoT). By harvesting ambient energy, battery-free devices can perform sensing intermittently without maintenance, thus significantly improving IoT sustainability. To build a practical intermittently-powered sensing system, efficient routing across battery-free devices for data delivery is essential. However, the intermittency of these devices brings new challenges, rendering existing routing protocols inapplicable.In this paper, we propose RICS, a new routing scheme tailored for intermittently-powered sensing systems. RICS features two major designs to combat the intermittency challenge, with the goal of achieving low-latency data delivery on a network built with battery-free devices. First, RICS incorporates a fast topology construction protocol for each IC node to establish a path towards the sink node with the least hop count. Second, RICS employs a low-latency message forwarding protocol, which incorporates an efficient synchronization mechanism and a novel technique called pendulum-sync to avoid time-consuming repeated node synchronization. Our evaluation based on an implementation in OMNeT ++ and comprehensive experiments with varying system settings shows that RICS can achieve orders of magnitude latency reduction in data delivery compared with the state-of-the-art.

Light field (LF) cameras, which can record real-word scenes from multiple viewpoints in a single shot, are widely used in 3D reconstruction, re-focusing, and virtual realityetc. However, the inherent trade-off between spatial resolution and angular resolution of LF images hinders their applications for scenarios requiring high resolutions. In this paper, we propose a novel intra-inter view interaction network for LF image super-resolution, termed as LF-IINet, to exploit the correlations among all views and simultaneously preserve the parallax structure of LF views. The proposed LF-IINet consists of two parallel branches. Specifically, the top branch extracts global inter-view information, and the bottom branch first independently maps each view to deep representations and then models the correlations among all intra-view features via proposed multi-view context block (MCB). The two branches interact with each other by proposed inter-assist-intra feature updating module (IntraFUM, where the intra feature are updated with the assistance of the inter feature) and intra-assist-inter feature updating module (InterFUM, where the inter feature are updated with the assistance of the intra feature). In this way, our LF-IINet incorporates rich angular and spatial information for LF image super-resolution. Extensive comparison with state-of-the-art methods demonstrates that our method achieves superior performance visually and quantitatively. Furthermore, quantitative results also show that our method is effective for LF images with either small or large disparities. Our code is shared inhttps://github.com/GaoshengLiu/LF-IINet.

The acquisition of densely-sampled light field (LF) images is costly, which hampers the applications of LF imaging technology in 3D reconstruction, digital refocusing, virtual reality,etc. To mitigate the obstacle, various approaches have been proposed to reconstruct densely-sampled LF images from sparsely-sampled ones. However, most existing methods still suffer from the non-Lambertian effect and large disparity issue. In this paper, we embrace the challenges by introducing a new paradigm for LF angular super-resolution (SR), which first explores the multi-scale spatial-angular correlations on the sparse sub-aperture images (SAIs) and then performs angular SR on macro-pixel features. In this way, we propose an efficient LF angular SR network, termed as EASR, with simple 3D (2D) CNNs and reshaping operations. The proposed EASR can extract effective feature representations on SAIs and can handle large disparities well by performing angular SR on macro-pixel features. Extensive comparisons with state-of-the-art methods demonstrate that our method achieves superior performance visually and quantitatively. Furthermore, our method achieves efficient angular SR by providing an excellent tradeoff between reconstruction performance and inference time.