Over the past several decades, artificial intelligence (AI) has been tremendously boosted by new algorithm designs, exponentially increased computing power, and an immense volume of calculation materials (i.e., data). Nevertheless, appropriate feature fusion and high-level, abstract forms of knowledge representation are required to help AI to achieve better results, as the primary goal of AI research is to enable machines to perform complex tasks that would typically require human intelligence.

Restoration and perception enhancement techniques are active research areas in robotics which play essential roles in helping us to perceive and understand the world. Their applications include human activity recognition, surgical medicine, geoinformatics, and remote sensing analysis.

Artificial intelligence based on computer vision has been greatly strengthened and developed, becoming one of the most important developing areas in robotics. Object recognition, classification, segmentation, topology, network, efficiency, navigation, and search based on spatial attributes are also anticipated to become important and valuable fields of development in artificial intelligence and robotics in the future.

Recently, intelligent reasoning has been widely used to address the significant technical issues involved in implementing AI in real-world applications, such as intelligent medical care, environmental analysis and prediction, autonomous driving, intelligent transportation, text classification, recommended systems, machine translation, and analog dialogues.

In this Special Issue, we present groundbreaking research and case studies that demonstrate the future applications of and advances in artificial intelligence and robotics, especially regarding visual and spatial perception enhancement and reasoning.

Yungyao Chen et al. (Contribution 1) introduce HDRFormer, an innovative framework designed to enhance high dynamic range (HDR) image quality in edge cloud-based video surveillance systems. Leveraging advanced deep learning algorithms and Internet of Things (IoT) technology, HDRFormer employs a unique architecture comprising a feature extraction module (FEM) and a weighted attention module (WAM). The FEM leverages a transformer-based hierarchical structure to adeptly capture multiscale image information. In addition, guided filters are utilized to steer the network, thereby enhancing the structural integrity of the images. On the other hand, the WAM focuses on reconstructing saturated areas, improving the perceptual quality of the images and rendering natural, saturated reconstructed HDR images. In addition, the framework exhibits outstanding performance in multiscale structural similarity (MS-SSIM) and HDR visual difference predictor (HDR-VDP2.2). The proposed method not only outperforms the existing HDR reconstruction techniques [1,2], but also offers better generalization capabilities, laying a robust foundation for future applications in smart cities.