Qun Hao

The human visual system is inherently blind to infrared radiation due to the insufficient energy of infrared photons to trigger the photoisomerization of the retinal chromophore, resulting in the loss of over half of the solar spectrum. Here, we report a colloidal quantum dot (CQD)-based infrared-to-visible upconverter that enables ultrasensitive upconversion of multispectral infrared light radiation as full-color visible vision. The photon-energy-selective excitonic transitions in quantum-confined states and the photon flux, respectively, enable infrared wavelength- and intensity-dependent number of photogenerated carriers. A dual-emissive-layer organic architecture with the strategically engineered hole-injection barrier could route these carriers into distinct color emission channels, resulting in correlated wavelength/intensity-to-color/luminance mapping. This paradigm shift yields a discrimination sensitivity for subtle infrared variations exceeding more than two orders of magnitude higher than conventional single-color modes, capitalizing on the human eye’s intrinsic superiority in chromatic differentiation over only brightness contrast. The resulting upconverter exhibits broadband detection extending beyond 2 μm, luminance exceeding 700 cd m ⁻² , and a photon-to-photon conversion efficiency of 3.85%. The upconverter could be applied as a lightweight, semi-transparent wearable eyeglass that projects multispectral infrared as full-color vision directly onto the retina. Besides, upconverters bound to light-sensitive proteins, potentially as an implantable retinal photoreceptor, confer innate infrared vision. By surpassing the evolutionary constraints of natural vision, this work establishes a versatile foundation for next-generation visual prosthetics and human-integrated sensory expansion.

Autonomous high-fidelity object reconstruction is fundamental for creating digital assets and bridging the simulation-to-reality gap in robotics. We present ObjSplat, an active reconstruction framework that leverages Gaussian surfels as a unified representation to progressively reconstruct unknown objects with both photorealistic appearance and accurate geometry. Addressing the limitations of conventional opacity or depth-based cues, we introduce a geometry-aware viewpoint evaluation pipeline that explicitly models back-face visibility and occlusion-aware multi-view covisibility, reliably identifying under-reconstructed regions even on geometrically complex objects. Furthermore, to overcome the limitations of greedy planning strategies, ObjSplat employs a next-best-path (NBP) planner that performs multi-step lookahead on a dynamically constructed spatial graph. By jointly optimizing information gain and movement cost, this planner generates globally efficient trajectories. Extensive experiments in simulation and on real-world cultural artifacts demonstrate that ObjSplat produces physically consistent models within minutes, achieving superior reconstruction fidelity and surface completeness while significantly reducing scan time and path length compared to state-of-the-art approaches.

The star tracker is the most accurate attitude determination device that guarantees the success of a spacecraft mission. On-orbit calibration of star trackers is essential to correct the deviation of geometric parameters caused by fluctuations of space-environment conditions. However, traditional calibration methods are mostly based on parametric models and involve iterative optimization calculations, making it difficult to effectively correct full-field distortion and achieve autonomous on-orbit calibration. Therefore, we developed a full-field numerical on-orbit autonomous calibration method. During the on-orbit stable operation, the satellite drives star trackers to scan and image the star map, while the star trackers continuously accumulate residuals between the measured and theoretical star positions. When stars cover the entire field of view, the refined grid-based numerical calibration model of the distortion residuals is established. Subsequently, the precise correction of the full-field distortion is performed via bilinear interpolation. The proposed method has been validated in SuperView Neo1-03. The mean distortion residuals after calibration are reduced to approximately 0.07 pixels. The attitude determination accuracy can achieve 0.321″(1σ). The method enabled the satellite to become China's commercial optical remote sensing satellite with the highest geolocation accuracy.

Distributed radar can provide an efficient detection performance by accumulating signals from multiple widely separated radars. However, distributed radar systems usually face communication constraints, including bandwidth limitation, communication delay, packet loss, and bit error, which impede practical application. To the best of the authors' knowledge, investigating the distributed detection problem with joint consideration of the above constraints is still an unexplored topic. Consequently, this paper studies the target detection problem in distributed radar systems, and these constraints are simultaneously addressed. First, to mitigate the communication burden, the least squares quantization is considered, and a computationally efficient algorithm is proposed to calculate the quantization parameters, enabling low-bit transmission to a fusion center (FC). Second, by incorporating the communication constraints into the communication channels, we formulate the model of the received test statistics at the FC, which are shown as a function of the constraints. Finally, two detectors are developed based on the Neyman-Pearson and nonparametric fusion criteria, and theoretical performance analyses of both detectors are included. Simulation results indicate that the proposed algorithm can significantly improve the computational efficiency in solving quantization parameters, and compared to clairvoyant fusion, the proposed detectors with 3-bit quantization merely cause 0.12 dB signal-to-noise ratio loss.

Infrared image colorization has gained widespread attention in recent years as an important means of enhancing image visibility and semantic expression. However, existing evaluation methods mostly rely on pixel-level differences or feature distribution distances, failing to comprehensively reflect the usability of colorization results in practical tasks. To address this, we propose a task-oriented colorization quality evaluation metric called Recognition-Task based Detection Score (RDS), which uses the recognition accuracy of object detection models on colorized images as a proxy indicator to measure their actual performance in downstream tasks, thereby achieving consistency between image quality assessment and task performance. RDS incorporates three key characteristics in its design: enhancing position robustness through the matching mechanism of object detection tasks, providing fine-grained interpretability through category-level accuracy calculation, and achieving task adjustability through flexible category division strategies. Systematic experiments conducted on both NIR–RGB and FLIR-5C datasets demonstrate that RDS maintains good subjective–objective consistency with traditional metrics under standard registration conditions, exhibits superior stability under registration error scenarios, and possesses fine-grained interpretability and task adjustability that traditional metrics lack. RDS maintains a 5.7% improvement in discriminative Score Gap under misalignment while PSNR degrades by 69.8%, and flexible category merging raises TIC-CGAN’s RDS from 76.05% to 96.45% on unseen scenes, providing more practically valuable criteria for the evaluation and optimization of infrared colorization models.

Multispectral infrared detection plays a crucial role in advanced applications spanning environmental monitoring, military surveillance, and biomedical diagnostics, offering superior target identification accuracy compared to single-band imaging techniques. In this work, we synthesized four distinct bands of colloidal quantum dots (CQDs)—specifically, a cut-off of 1.3 µm with PbS CQDs and 1.8 µm, 2.6 µm, and 3.5 µm with HgTe CQDs—and employed them to construct planar multiband infrared photodetectors. The device exhibited a clear photoresponse at room temperature from 0.8 µm to 3.5 µm, with responsivity of 5.39 A/W and specific detectivity of 2.01 × 10¹¹ Jones at 1.8 µm. This materials–device co-design strategy integrates wavelength-selective CQD synthesis with planar pixel-level patterning, providing a versatile pathway for developing low-cost, solution-processed, multiband infrared photodetectors.