Prior Images Guided Generative Autoencoder Model for Dual-Camera Compressive Spectral Imaging
IEEE Transactions on Circuits and Systems for Video Technology(2024)
摘要
Compressive Spectral Imaging (CSI) techniques have attracted considerable attention among researchers for their ability to simultaneously capture spatial and spectral information using low-cost, compact optical components. A prominent example of CSI techniques is the Dual-Camera Coded Aperture Snapshot Spectral Imaging (DC-CASSI), which involves reconstructing hyperspectral images from CASSI measurements and uncoded panchromatic or RGB images. Despite its significance, the reconstruction process in DC-CASSI is challenging. Conventional DC-CASSI techniques rely on different models to explore the similarity between uncoded images and hyperspectral images. Nevertheless, two main issues persist: i) the effective utilization of
spatial information
from RGB images to guide the reconstruction process, and ii) the enhancement of
spectral consistency
of recovered images when using panchromatic/RGB images, which inherently lack precise spectral information. To address these challenges, we propose a novel Prior images guided generative autoEncoder (PiE) model. The PiE model leverages RGB images as prior information to enhance spatial details and designs a generative model to improve spectral quality. Notably, the generative model is optimized in a self-supervised manner. Comprehensive experimental results demonstrate that the proposed PiE method outperforms existing techniques, achieving state-of-the-art performance.
更多查看译文
关键词
Compressive Spectral Imaging,Snapshot Imaging,Computational Imaging,Compressive Sensing
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要