High-speed imaging, which captures the fleeting dynamics of moving objects at extreme frame rates, has become an indispensable tool across a wide range of scientific disciplines. Yet, the pursuit of high temporal resolution often comes at the cost of significant image degradation, due to the inherent limitations of imaging sensors and the extreme conditions of ultra-short exposure and massive data throughput. As a result, high-speed cameras often produce images marred by strong noise and severe color distortions. In this work, we propose a deep image signal processing (ISP) paradigm that enables high-speed cameras to maintain extremely high frame rates while achieving image quality comparable to that of digital single-lens reflex (DSLR) cameras. To this end, we make two key contributions: 1) constructing RHID, the first large-scale real-world high-speed imaging ISP dataset, comprising 282,912 RAW images captured by high-speed cameras and corresponding sRGB images captured by DSLRs, featuring complex degradations intrinsic to high-speed acquisition; and 2) proposing a misalignment-robust ISP learning framework (MisISP), equipped with a prior mapper-guided image alignment module (PMIA) and a spectrum-guided weakly-aligned image supervisory loss, which effectively addresses inherent pixel misalignments caused by heterogeneous sensor characteristics. Extensive experiments demonstrate that our paradigm substantially advances the performance of existing deep ISP models for high-speed imaging, achieving remarkable improvements in noise suppression, brightness enhancement, and color preservation.

Deep learning-based approaches, which learn pixel-to-pixel mapping from input to output images, have demonstrated exceptional performance in enhancing low-quality fundus images. However, due to the ambiguous definition of the ground-truth high-quality image, the pixel-to-pixel mapping encounters an ill-posed problem arising from the complex one-to-many relationship between low-quality fundus images and their corresponding high-quality versions. To address this problem, this work proposes a PCFlow, the first normalizing flow method that learns the complex distributions of high-quality fundus images rather than a pixel-to-pixel mapping. Unlike the existing image natural enhancement methods that aim to restore images with comfortable visual quality, PCFlow enhances fundus images by prioritizing clinically significant information. To this end, we design a condition module that utilizes retinal structure as a conditioning factor to constrain the optimization of PCFlow, and then build an invertible coupling layer that employs a pyramid structure for identifying each frequency component of retinal features. With the cooperation and interactions of these key components, the proposed PCFlow preserves the retinal structures and pathological characteristics essential for clinical applications. Extensive experiments on the real and synthetic fundus datasets demonstrate that our method achieves better performance.

Traditional auto white balance (AWB) algorithms typically assume a single global illuminant source, which leads to color distortions in multi-illuminant scenes. While recent neural network-based methods have shown excellent accuracy in such scenarios, their high parameter count and computational demands limit their practicality for real-time video applications. The Fast Fourier Color Constancy (FFCC) algorithm was proposed for single-illuminant-source scenes, predicting a global illuminant source with high efficiency. However, it cannot be directly applied to multi-illuminant scenarios unless specifically modified. To address this, we propose Integral Fast Fourier Color Constancy (IFFCC), an extension of FFCC tailored for multi-illuminant scenes. IFFCC leverages the proposed integral UV histogram to accelerate histogram computations across all possible regions in Cartesian space and parallelizes Fourier-based convolution operations, resulting in a spatially-smooth illumination map. This approach enables high-accuracy, real-time AWB in multi-illuminant scenes. Extensive experiments show that IFFCC achieves accuracy that is on par with or surpasses that of pixel-level neural networks, while reducing the parameter count by over 400× and processing speed by 20 − 100× faster than network-based approaches.

Vehicle‐borne battery condition is an important factor affecting the efficiency of the maglev train operation and other connected ones. To effectively eliminate the influence of the battery condition and improve the operation efficiency of the connected maglev trains, an operation control strategy is proposed to guarantee train operation safety. First, based on Internet of Things, a sensor network is designed to monitor vehicle‐borne battery condition in each vehicle of the train. Second, the train Operation Control System collects battery data of all vehicles in a maglev train by Train Communication Network. Third, all connected maglev trains share the battery data via a 38 GHz directional Radio Communication System and adjust operation control strategy accordingly. Simulation results indicate that the proposed strategy can guarantee the operation safety of the connected maglev trains.