Yihong Leng

Yihong Leng, Siming Zheng, Jinwei Chen et al.

Event cameras provide sparse yet temporally high-resolution motion information, demonstrating great potential for motion deblurring. However, the delicate events are highly susceptible to noise. Although noise can be reduced by raising the threshold of Dynamic Vision Sensors (DVS), this inevitably causes under-reporting of events. Most existing event-guided deblurring methods overlook this practical trade-off, and the indiscriminate feature extraction and naive fusion result in unstable and mixed representations and ultimately unsatisfactory performance. To tackle these challenges, we propose a Robust Event-guided Deblurring (RED) network with modality-specific disentangled representation. First, we introduce a Robustness-Oriented Perturbation Strategy (RPS) that mimics various DVS thresholds, exposing RED to diverse under-reporting patterns and thereby fostering robustness under unknown conditions. With an adaption to RPS, a Modality-specific Representation Mechanism (MRM) is designed to explicitly model semantic understanding, motion priors, and cross-modality correlations from two inherently distinct but complementary sources: blurry images and partially disrupted events. Building on these reliable features, two interactive modules are presented to enhance motion-sensitive areas in blurry images and inject semantic context into under-reporting event representations. Extensive experiments on synthetic and real-world datasets demonstrate RED consistently achieves state-of-the-art performance in terms of both accuracy and robustness.

Yihong Leng, Jiaojiao Li, Haitao Xu et al.

Current hyperspectral image (HSI) reconstruction methods primarily rely on image-level approaches, which are time-consuming to form abundant high-quality HSIs through imagers. In contrast, spectrometers offer a more efficient alternative by capturing high-fidelity point spectra, enabling pixel-level HSI reconstruction that balances accuracy and label efficiency. To this end, we introduce a pixel-level spectral super-resolution (Pixel-SSR) paradigm that reconstructs HSI from RGB and point spectra. Despite its advantages, Pixel-SSR presents two key challenges: 1) generalizability to novel scenes lacking point spectra, and 2) effective information extraction to promote reconstruction accuracy. To address the first challenge, a Gamma-modeled strategy is investigated to synthesize point spectra based on their intrinsic properties, including nonnegativity, a skewed distribution, and a positive correlation. Furthermore, complementary three-branch prompts from RGB and point spectra are extracted with a Dynamic Prompt Mamba (DyPro-Mamba), which progressively directs the reconstruction with global spatial distributions, edge details, and spectral dependency. Comprehensive evaluations, including horizontal comparisons with leading methods and vertical assessments across unsupervised and image-level supervised paradigms, demonstrate that ours achieves competitive reconstruction accuracy with efficient label consumption.