DSConv: Dynamic Splitting Convolution for Pansharpening

Aiming to obtain a high-resolution image, pansharpening involves the fusion of a multi-spectral image (MS) and a panchromatic image (PAN), the low-level vision task remaining significant and challenging in contemporary research. Most existing approaches rely predominantly on standard convolutions, few making the effort to adaptive convolutions, which are effective owing to the inter-pixel correlations of remote sensing images. In this paper, we propose a novel strategy for dynamically splitting convolution kernels in conjunction with attention, selecting positions of interest, and splitting the original convolution kernel into multiple smaller kernels, named DSConv. The proposed DSConv more effectively extracts features of different positions within the receptive field, enhancing the network's generalization, optimization, and feature representation capabilities. Furthermore, we innovate and enrich concepts of dynamic splitting convolution and provide a novel network architecture for pansharpening capable of achieving the tasks more efficiently, building upon this methodology. Adequate fair experiments illustrate the effectiveness and the state-of-the-art performance attained by DSConv.Comprehensive and rigorous discussions proved the superiority and optimal usage conditions of DSConv.

STAR-RIS-Enabled Multi-Path Beam Routing with Passive Beam Splitting

Reconfigurable intelligent surfaces (RISs) can be densely deployed in the environment to create multi-reflection line-of-sight (LoS) links for signal coverage enhancement. However, conventional reflection-only RISs can only achieve half-space reflection, which limits the LoS path diversity. In contrast, simultaneously transmitting and reflecting RISs (STAR-RISs) can achieve full-space reflection and transmission, thereby creating more LoS paths. Hence, in this paper, we study a new multi-STAR-RIS-aided communication system, where a multi-antenna base station (BS) transmits to multiple single-antenna users by exploiting the signal beam routing over a set of cascaded LoS paths each formed by multiple STAR-RISs. To reveal essential insights, we first consider a simplified single-user case, aiming to maximize its received signal power by jointly optimizing the active beamforming at the BS, the BS's power allocation over different paths, the number of selected beam-routing paths, the selected STAR-RISs for each path, as well as their amplitude and phase shifts for transmission/reflection. However, this problem is difficult to be optimally solved as different paths may be intricately coupled at their shared STAR-RISs. To tackle this difficulty, we first derive the optimal solution to this problem in closed-form for a given set of paths. The clique-based approach in graph theory is then applied to solve the remaining multi-path selection problem efficiently. Next, we extend the proposed clique-based method to the multi-user case to maximize the minimum received signal power among all users, subject to additional constraints on the disjointness of the selected paths for different users. Simulation results show that our proposed STAR-RIS-enabled beam routing outperforms the conventional beam routing with reflection-only RISs in both single- and multi-user cases.