Dynamic Scene Reconstruction for Color Spike Camera via Zero-Shot Learning

IF 4.2 2区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Computational Imaging Pub Date : 2025-01-10 DOI:10.1109/TCI.2025.3527156
Yanchen Dong;Ruiqin Xiong;Xiaopeng Fan;Shuyuan Zhu;Jin Wang;Tiejun Huang
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Abstract

As a neuromorphic vision sensor with ultra-high temporal resolution, spike camera shows great potential in high-speed imaging. To capture color information of dynamic scenes, color spike camera (CSC) has been invented with a Bayer-pattern color filter array (CFA) on the sensor. Some spike camera reconstruction methods try to train end-to-end models by massive synthetic data pairs. However, there are gaps between synthetic and real-world captured data. The distribution of training data impacts model generalizability. In this paper, we propose a zero-shot learning-based method for CSC reconstruction to restore color images from a Bayer-pattern spike stream without pre-training. As the Bayer-pattern spike stream consists of binary signal arrays with missing pixels, we propose to leverage temporally neighboring spike signals of frame, pixel and interval levels to restore color channels. In particular, we employ a zero-shot learning-based scheme to iteratively refine the output via temporally neighboring spike stream clips. To generate high-quality pseudo-labels, we propose to exploit temporally neighboring pixels along the motion direction to estimate the missing pixels. Besides, a temporally neighboring spike interval-based representation is developed to extract temporal and color features from the binary Bayer-pattern spike stream. Experimental results on real-world captured data demonstrate that our method can restore color images with better visual quality than compared methods.
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IEEE Transactions on Computational Imaging
IEEE Transactions on Computational Imaging Mathematics-Computational Mathematics
CiteScore
8.20
自引率
7.40%
发文量
59
期刊介绍: The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.
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