Near-Infrared (NIR) hyperspectral imaging opens up numerous possibilities for wide applications. Despite Compressive Spectral Imaging (CSI) being a promising technique, which enables the acquisition of three-dimensional (3D) spatio-spectral information from dynamic scenes, applying it to the NIR spectrum remains challenging. The bottleneck lies in the high cost and limited resolution of InGaAs Focal Plane Arrays (FPAs), which further degrade the high-frequency information of the compressed measurements. Here we demonstrate a novel Effective Prior Image-guided Spectral imager, termed EpiSpec, towards high-resolution spectral imaging in the NIR. Our key observation is that the tail response of low-cost silicon-based sensors tends to capture similar image, offering high spatial resolution guidance for retrieving details. Hence, the degraded measurement of hyperspectral scene, guided by the prior image, is capable of obtaining high-quality reconstructions. Since the prior image integrates only a partial spectrum of the target scene, introducing content-aware chromatic errors, we propose the Prior Image Guided Deep Unfolding Framework (PIUF) for high-fidelity spectral reconstruction. This framework implicitly models the underlying non-linear relationship between the degraded measurements and the Non-Panchromatic (NPA) prior image. We also introduce a new NIR Spectral Images Dataset (NISID), which features a broad selection of real-world NIR spectral interesting scenes. Based on the dataset in hand, we evaluate the sparse structure of such spectra, which can serve as a guide for efficient CSI sensing matrices design. Extensive evaluations on representative CSI systems demonstrate the effectiveness of the proposed EpiSpec framework. Subsequently, lab prototypes are built for real-world imaging validation, further supporting the viability of high-resolution spectral imaging in the NIR.

We endeavor on a rarely explored task named thermal infrared video denoising. Perception in the thermal infrared significantly enhances the capabilities of machine vision. Nonetheless, noise in imaging systems is one of the factors that hampers the large-scale application of equipment. Existing thermal infrared denoising methods, primarily focusing on the image level, inadequately utilize time-domain information and insufficiently conduct investigation of system-level mixed noise, presenting the inferior ability in the video-recorded era; while video denoising methods, commonly applied to RGB cameras, exhibit uncertain effectiveness owing to substantial dissimilarities in the noise models and modalities between RGB and thermal infrared images. In sight of this, we initially revisit the imaging mechanism, while concurrently introducing a physics-inspired noise generator based on the sources and characteristics of system noise. Subsequently, a thermal infrared video denoising dataset consisting of 518 real-world videos is constructed. Lastly, we propose a denoising model called multi-domain infrared video denoising network, capable of concentrating features from the time, space, and frequency domains to restore high-fidelity videos. Extensive experiments demonstrate that the proposed method achieves state-of-the-art denoising quality and can be successfully applied to commercial cameras and downstream vision tasks, providing a new avenue for clear videography in the thermal infrared world. The dataset and code will be available.

The demand for cone-beam computed tomography (CBCT) imaging in clinics, particularly in dentistry, is rapidly increasing. Preoperative surgical planning is crucial to achieving desired treatment outcomes for imaging-guided surgical navigation. However, the lack of surface texture hinders effective communication between clinicians and patients, and the accuracy of superimposing a textured surface onto CBCT volume is limited by dissimilarity and registration based on facial features. To address these issues, this study presents a CBCT imaging system integrated with a monocular camera for reconstructing the texture surface by mapping it onto a 3D surface model created from CBCT images. The proposed method utilizes a geometric calibration tool for accurate mapping of the camera-visible surface with the mosaic texture. Additionally, a novel approach using 3D-2D feature mapping and surface parameterization technology is proposed for texture surface reconstruction. Experimental results, obtained from both real and simulation data, validate the effectiveness of the proposed approach with an error reduction to 0.32 mm and automated generation of integrated images. These findings demonstrate the robustness and high accuracy of our approach, improving the performance of texture mapping in CBCT imaging.