Efficient One-Step Diffusion Refinement for Snapshot Compressive Imaging

• URL: http://arxiv.org/abs/2409.07417v1
• Date: Wed, 11 Sep 2024 17:02:10 GMT
• Title: Efficient One-Step Diffusion Refinement for Snapshot Compressive Imaging
• Authors: Yunzhen Wang, Haijin Zeng, Shaoguang Huang, Hongyu Chen, Hongyan Zhang,
• Abstract summary: Coded Aperture Snapshot Spectral Imaging (CASSI) is a crucial technique for capturing three-dimensional multispectral images (MSIs) Current state-of-the-art methods, predominantly end-to-end, face limitations in reconstructing high-frequency details. This paper introduces a novel one-step Diffusion Probabilistic Model within a self-supervised adaptation framework for Snapshot Compressive Imaging.
• Score: 8.819370643243012
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Coded Aperture Snapshot Spectral Imaging (CASSI) is a crucial technique for capturing three-dimensional multispectral images (MSIs) through the complex inverse task of reconstructing these images from coded two-dimensional measurements. Current state-of-the-art methods, predominantly end-to-end, face limitations in reconstructing high-frequency details and often rely on constrained datasets like KAIST and CAVE, resulting in models with poor generalizability. In response to these challenges, this paper introduces a novel one-step Diffusion Probabilistic Model within a self-supervised adaptation framework for Snapshot Compressive Imaging (SCI). Our approach leverages a pretrained SCI reconstruction network to generate initial predictions from two-dimensional measurements. Subsequently, a one-step diffusion model produces high-frequency residuals to enhance these initial predictions. Additionally, acknowledging the high costs associated with collecting MSIs, we develop a self-supervised paradigm based on the Equivariant Imaging (EI) framework. Experimental results validate the superiority of our model compared to previous methods, showcasing its simplicity and adaptability to various end-to-end or unfolding techniques.

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