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Towards Dependable Optical Flow: Benchmarks for Real-World Spatiotemporal Corruptions
Zhonghua Yi, Hao Shi, Qi Jiang +5
Optical flow estimation is a fundamental task in dynamic scene understanding and computer vision. While recent deep models achieve impressive accuracy on clean benchmarks, their ro…
EI-Nexus: Towards Unmediated and Flexible Inter-Modality Local Feature Extraction and Matching for Event-Image Data
Zhonghua Yi, Hao Shi, Qi Jiang +5
Event cameras, with high temporal resolution and high dynamic range, have limited research on the inter-modality local feature extraction and matching of event-image data. We propo…
Towards Single-Lens Controllable Depth-of-Field Imaging via Depth-Aware Point Spread Functions
Xiaolong Qian, Qi Jiang, Yao Gao +8
Controllable Depth-of-Field (DoF) imaging commonly produces amazing visual effects based on heavy and expensive high-end lenses. However, confronted with the increasing demand for…
OmniLens: Towards Universal Lens Aberration Correction via LensLib-to-Specific Domain Adaptation
Qi Jiang, Yao Gao, Shaohua Gao +7
Emerging universal Computational Aberration Correction (CAC) paradigms provide an inspiring solution to light-weight and high-quality imaging with a universal model trained on a le…
Exploring Quasi-Global Solutions to Compound Lens Based Computational Imaging Systems
Yao Gao, Qi Jiang, Shaohua Gao +3
Recently, joint design approaches that simultaneously optimize optical systems and downstream algorithms through data-driven learning have demonstrated superior performance over tr…
Representing Domain-Mixing Optical Degradation for Real-World Computational Aberration Correction via Vector Quantization
Qi Jiang, Zhonghua Yi, Shaohua Gao +8
Relying on paired synthetic data, existing learning-based Computational Aberration Correction (CAC) methods are confronted with the intricate and multifaceted synthetic-to-real dom…