This article aims to present a barrier function based adaptive sliding mode (BFASM) control scheme for a class of uncertain nonlinear systems with actuator saturation. In conventional adaptive sliding mode control for saturated systems, the adaptive gain is often monotonically increasing with respect to the persistent disturbance, which results in aggressive control input with severe chattering that is often giving rise to system instability. In order to solve this problem, this article proposes a BFASM controller with explicit consideration of actuator saturation and uncertainties. The proposed method contains a reaching control input that can adapt to the time-varying disturbances. In addition, the proposed method does not require the upper bound information of disturbance that is commonly requested in conventional sliding mode control and thus removes the conservative assumption on a sufficiently large control gain. Stability analysis proves that the tracking error under the proposed control can asymptotically converge into a prespecified region in the presence of actuator saturation, parameter uncertainties, and external disturbances. The proposed method is also applied to an air-floating positioning system with input saturation, and experimental results demonstrate its efficacy.

Deep generative models have gained considerable attention in low-level vision tasks due to their powerful generative capabilities. Among these, diffusion model-based approaches, which employ a forward diffusion process to degrade an image and a reverse denoising process for image generation, have become particularly prominent for producing high-quality, diverse samples with intricate texture details. Despite their widespread success in low-level vision, there remains a lack of a comprehensive, insightful survey that synthesizes and organizes the advances in diffusion model-based techniques. To address this gap, this paper presents the first comprehensive review focused on denoising diffusion models applied to low-level vision tasks, covering both theoretical and practical contributions. We outline three general diffusion modeling frameworks and explore their connections with other popular deep generative models, establishing a solid theoretical foundation for subsequent analysis. We then categorize diffusion models used in low-level vision tasks from multiple perspectives, considering both the underlying framework and the target application. Beyond natural image processing, we also summarize diffusion models applied to other low-level vision domains, including medical imaging, remote sensing, and video processing. Additionally, we provide an overview of widely used benchmarks and evaluation metrics in low-level vision tasks. Our review includes an extensive evaluation of diffusion model-based techniques across six representative tasks, with both quantitative and qualitative analysis. Finally, we highlight the limitations of current diffusion models and propose four promising directions for future research. This comprehensive review aims to foster a deeper understanding of the role of denoising diffusion models in low-level vision. For those interested, a curated list of diffusion model-based techniques, datasets, and related information across over 20 low-level vision tasks is available at https://github.com/ChunmingHe/awesome-diffusion-models-in-low-level-vision.

Automated radiographic report generation is challenging in at least two aspects. First, medical images are very similar to each other and the visual differences of clinic importance are often fine-grained. Second, the disease-related words may be submerged by many similar sentences describing the common content of the images, causing the abnormal to be misinterpreted as the normal in the worst case. To tackle these challenges, this paper proposes a pure transformer-based framework to jointly enforce better visual-textual alignment, multi-label diagnostic classification, and word importance weighting, to facilitate report generation. To the best of our knowledge, this is the first pure transformer-based framework for medical report generation, which enjoys the capacity of transformer in learning long range dependencies for both image regions and sentence words. Specifically, for the first challenge, we design a novel mechanism to embed an auxiliary image-text matching objective into the transformer’s encoder-decoder structure, so that better correlated image and text features could be learned to help a report to discriminate similar images. For the second challenge, we integrate an additional multi-label classification task into our framework to guide the model in making correct diagnostic predictions. Also, a term-weighting scheme is proposed to reflect the importance of words for training so that our model would not miss key discriminative information. Our work achieves promising performance over the state-of-the-arts on two benchmark datasets, including the largest dataset MIMIC-CXR.