Detecting tiny objects in remote sensing images has always been a challenging and intensive research area. This problem has not been well solved due to the fact that object detection in remote sensing images is characterized by large scale variations and complex backgrounds. On this basis, we propose the EMS-Net constructed based on YOLOv8s for tiny object detection network in remote sensing images. First, a new module multi-branch context aggregation(MCA) is proposed to improve deep feature extraction and deep feature fusion of the model. In addition, we use our self-designed multi-scale feature communication module (MFCM) aimed at reducing the loss of semantic information of object and mitigating the obstruction of foreground object by complex background. Finally, Wise IoU-Normalized Wasserstein distance (WIoU-NWD) is used as the bounding box regression loss to adapt the model to different object scale while improving the ability to localize tiny object. Comprehensive experiments on three popular datasets demonstrate that our method outperforms existing detectors, particularly in detecting tiny objects. Specifically, our approach achieves the mean average precision (mAP) of 77.2% on the DIOR dataset, 96.7% on the RSOD dataset and 75.1% on the DOTA-v1.5 dataset.

In this paper, we present a novel sparse Bayesian learning (SBL) framework for large-scale image recovery. We formulate variational Bayesian (VB) and generalized approximate message passing (GAMP) into the SBL model (called VGAMP-SBL) to speed up image reconstruction. GAMP can be argued a scalar estimation function described by a set of simple state evolution (SE) equations. From the SE equations, one can accurately predict the values of SBL Params, while it can obtain better reconstruction results without matrix inversion. Moreover, the interaction between data fluctuations and parameter fluctuations is negligible in VB structure, so the maximum marginal likelihood function can be easily obtained, This improves the computation efficiency of our algorithm greatly. Experimental results corroborate these claims.

There have been significant advances in capturing gigapixel panoramas (GPP). However, solutions for viewing GPPs on head-mounted displays (HMDs) are lagging: an immersive experience requires ultra-fast rendering while directly loading a GPP onto the GPU is infeasible due to limited texture memory capacity. In this paper, we present a novel out-of-core rendering technique that supports not only classic panning, tilting, and zooming but also dynamic refocusing for viewing a GPP on HMD. Inspired by the network package transmission mechanisms in distributed visualization, our approach employs hierarchical image tiling and on-demand data updates across the main and the GPU memory. We further present a multi-resolution rendering scheme and a refocused light field rendering technique based on RGBD GPPs with minimal memory overhead. Comprehensive experiments demonstrate that our technique is highly efficient and reliable, able to achieve ultra-high frame rates ( fps) even on low-end GPUs. With an embedded gaze tracker, our technique enables immersive panorama viewing experiences with unprecedented resolutions, field-of-view, and focus variations while maintaining smooth spatial, angular, and focal transitions.