20 accepted papers
Fine-grained temporal action segmentation plays a vital role in comprehensivehuman behavior understanding, with skeleton-based approaches (STAS) gaining prominence for their privacy and robustness. A core challenge in STAS arises from the conflicting feature requirements of action classification (de…
Spike cameras are a novel class of neuromorphic vision sensors that capture scene dynamics with ultra-high temporal resolution via spike planes. While recent methods have addressed motion blur and noise in spike-based reconstruction, defocus blur caused by shallow depth of field or lens adjustment d
Existing single-image denoising algorithms often struggle to restore details when dealing with complex noisy images. The introduction of near-infrared (NIR) images offers new possibilities for RGB image denoising. However, due to the inconsistency between NIR and RGB images, the existing works still…
Event cameras have shown promise in vision applications like optical flow estimation and stereo matching with many specialized architectures. However, existing works only focus event data within the confines of task-specific domains, overlooking the correlations between tasks across the temporal and…
Event cameras are innovative sensors that capture brightness changes as asynchronous events rather than traditional intensity frames. These cameras offer substantial advantages over conventional cameras, including high temporal resolution, high dynamic range, and the elimination of motion blur. Howe…
While diffusion models have demonstrated significant success in standard dynamic range (SDR) image synthesis, generating high dynamic range (HDR) images with higher luminance and broader color gamuts remains challenging. This arises primarily from two factors: (1) The incompatibility between pretrai…
Spike-based imaging, inspired by the human visual system, offers several advantages, including high temporal resolution and low power consumption, but suffers from significant image degradation in low-light conditions due to noise interference. Restoring spike images under such conditions poses a si…
Image denoising enhances image quality, serving as a foundational technique across various computational photography applications. The obstacle to clean image acquisition in real scenarios necessitates the development of self-supervised image denoising methods only depending on noisy images, especia…
Event cameras provide asynchronous, low-latency, and high-dynamic-range visual signals, making them ideal for real-time perception tasks such as object detection. However, effectively modeling the temporal dynamics of event streams remains a core challenge. Most existing methods follow frame-based d…
Dual-camera compressive hyperspectral imaging (DCCHI) offers the capability to reconstruct 3D hyperspectral image (HSI) by fusing compressive and panchromatic (PAN) image which has shown great potential for snapshot hyperspectral imaging in practice. In this paper we introduce a novel DCCHI reconstr…
Spike cameras leveraging spike-based integration sampling and high temporal resolution offer distinct advantages over standard cameras. However existing approaches reliant on spike cameras often assume optimal illumination a condition frequently unmet in real-world scenarios. To address this we intr…
"Event-based video reconstruction has garnered increasing attention due to its advantages, such as high dynamic range and rapid motion capture capabilities. However, current methods often prioritize the extraction of temporal information from continuous event flow, leading to an overemphasis on low-…
Diffractive snapshot hyperspectral imaging based on the deep optics framework has been striving to capture the spectral images of dynamic scenes. However, existing deep optics frameworks all suffer from the mismatch between the optical hardware and the reconstruction algorithm due to the quantizatio…
Snapshot hyperspectral imaging has been developed to capture the spectral information of dynamic scenes. In this paper, we propose a deep neural network by learning the tensor low-rank prior of hyperspectral images (HSI) in the feature domain to promote the reconstruction quality. Our method is insp…
Computational spectral imaging has been striving to capture the spectral information of the dynamic world in the last few decades. In this paper, we propose an interpretable neural network for computational spectral imaging. First, we introduce a novel data-driven prior that can adaptively exploit b…
Exploiting the prior information is fundamental for the image reconstruction in computational hyperspectral imaging. Existing methods usually unfold the 3D signal as a 1D vector and treat the prior information within different dimensions in an indiscriminative manner, which ignores the high-dimensio…
To solve the low spatial and/or temporal resolution problem which the conventional hypelrspectral cameras often suffer from, coded snapshot hyperspectral imaging systems have attracted more attention recently. Recovering a hyperspectral image (HSI) from its corresponding coded image is an ill-posed…
Regularization is a fundamental technique to solve an ill-posed optimization problem robustly and is essential to reconstruct compressive hyperspectral images. Various hand-crafted priors have been employed as a regularizer but are often insufficient to handle the wide variety of spectra of natural…
This paper presents the first snapshot hyperspectral light field imager in practice. Specifically, we design a novel hybrid camera system to obtain two complementary measurements that sample the angular and spectral dimensions respectively. To recover the full 5D hyperspectral light field from the s…
We propose a novel dual-camera design to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution. Our work has two key technical contributions. First, we build a dual-camera system that simultaneously captures a panchromatic video at a high frame rate and a hypers…