Intravital mesoscale imaging plays a crucial role in bridging the gap between cellular and organ-level investigations by enabling high-resolution visualization across large fields of view. Continuous advancements in optical microscopy have significantly improved imaging performance, yet fundamental challenges remain. Effective intravital mesoscale imaging requires a balance between spatial resolution, imaging speed, field of view, and while overcoming limitations such as scattering, aberrations, phototoxicity and photobleaching. This review summarizes key challenges in achieving high-performance intravital mesoscale optical imaging and provides an overview of advanced optical imaging techniques, including wide field, laser scanning, as well as computational imaging approaches. Despite these advancements, further improvements are necessary to address existing limitations and unlock new possibilities. Future developments will focus on enhancing imaging depth, further improving space bandwidth products, and integrating computational methods for real-time processing and large-scale data analysis, further advancing mesoscale imaging for biological research.

View-based 3-D object retrieval has become an emerging topic in recent years, especially with the fast development of visual content acquisition devices, such as mobile phones with cameras. Extensive research efforts have been dedicated to this task, while it is still difficult to measure the relevance between two objects with multiple views. In recent years, learning-based methods have been investigated in view-based 3-D object retrieval, such as graph-based learning. It is noted that the graph-based methods suffer from the high computational cost from the graph construction and the corresponding learning process. In this paper, we introduce a general framework to accelerate the learning-based view-based 3-D object matching in large scale data. Given a query object $Q$ and one object $O$ from a 3-D dataset $𝒟$, the first step is to extract a small set of candidate relevant 3-D objects for object $O$. Then multiple hypergraphs can be constructed based on this small set of 3-D objects and the learning on the fused hypergraph is conducted to generate the relevance between $Q$ and $O$, which can be further used in the retrieval procedure. Experiments demonstrate the effectiveness of the proposed framework.

An opportunistic automatic repeat request (ARQ) scheme was developed to support high-quality video transmission over cooperative decode-forward networks. The opportunistic ARQ is implemented in the form of distributed space-time block codes (STBCs) and the desired STBC for a certain channel state is determined by the diversity-multiplexing tradeoff principle. With good channel states, the source and relay may jointly retransmit the lost video packets using high-rate low-diversity codes while with bad channel states they will retransmit the lost video packets using low-rate high-diversity codes. Furthermore, an adaptive packet dropping mechanism was introduced by considering the delay constraint and layer dependency characteristics of scalable video streaming. Test results show that the scheme significantly improves the throughput and video quality compared with other schemes. Tests also indicate that the source-relay link condition plays an important role in affecting the scheme performance.