The majority of current remote sensing images possess high-radiometric resolution exceeding 10 bits. Precisely compressing this radiometric resolution to 8 bits is crucial for visualization and subsequent deep learning tasks. Previously, radiometric resolution compression required extensive parameter adjustments of traditional tone mapping operators. Deep learning is gradually replacing this high manual dependency method. However, existing deep learning tone mapping techniques are primarily designed for natural scene images captured by digital cameras, making direct application to remote sensing images challenging. This limitation stems from disparities in data formats and the complexity of semantic representation in remote sensing images. Moreover, the block prediction inherent in deep learning models often results in tiling artifacts post-splicing, failing to satisfy the scale dependency of remote sensing images. To tackle these challenges, we propose leveraging contrastive learning methods to compress the radiometric resolution of remote sensing images. Given the rich detail information and complex spatial distribution of objects in remote sensing images, we develop a CNN-Transformer hybrid generator capable of capturing both local details and long-range dependencies. Building upon this, we introduce non-local self-similarity contrastive loss and histogram similarity loss to enhance feature expression and regulate image color distribution. Additionally, we present a post-processing technique based on hybrid histogram matching to enhance image quality and seamlessly generate whole-scene images. Through experiments and comparisons on our dataset, our method demonstrates superior performance. The dataset and code can be obtained online in this link https://github.com/ZzzTD/RRCGAN.

This study addresses limitations in industrial defect detection by enhancing YOLOv8 with EfficientNet-B0. Using MBConv, multiscale pathways, and C2f modules, the model improved precision from 0.935 to 0.983, the recall from 0.917 to 0.981, [email protected] from 0.964 to 0.993, and [email protected]:0.95 from 0.787 to 0.9 on the NEU-DET dataset, enabling efficient, real-time defect detection for industrial applications.

In visual SLAM (VSLAM) systems, loop closure plays a crucial role in reducing accumulated errors. However, VSLAM systems relying on low-level visual features often suffer from the problem of perceptual confusion in repetitive environments, where scenes in different locations are incorrectly identified as the same. Existing work has attempted to introduce object-level features or artificial landmarks. The former approach struggles to distinguish visually similar but different objects, while the latter is both time-consuming and labor-intensive. This paper introduces a novel loop closure detection method that leverages pretrained AI foundation models to extract rich semantic information about specific types of objects (e.g., door numbers), referred to as semantic anchors, that help to distinguish similar scenes better. In settings such as office buildings, hotels, and warehouses, this approach helps to improve the robustness of loop closure detection. We validate the effectiveness of our method through experiments conducted in both simulated and real-world environments.

Protecting users' privacy when transmitting a large amount of data over the Internet is becoming increasingly important nowadays. In this paper, we focus on the streaming categorical information and propose a novel anonymization technique for providing a strong privacy protection to safeguard against privacy disclosure and information tampering. Our technique utilizes an innovative two-phase anonymization approach which is very easy to implement, highly efficient in terms of speed and communication and is robust against possible tampering from adversaries. Extensive experimental evaluation that is conducted demonstrates that our technique is very efficient and more robust than the existing method.