编码孔径快照光谱成像重构算法综述

Spectral images contain a wealth of spatial and spectral information, enabling them to effectively reflect an object's composition, structure, and material properties. They have significant application value in aerospace remote sensing, medical diagnosis, and machine vision. In recent years, spectral imaging technology has got significant attention and emerged as a prominent research area. Conventional spectral imaging techniques scan along the spatial or spectral dimensions, enabling the sequential acquisition of spectral information from the object's surface. Due to the extended exposure time, these techniques are unsuitable for capturing dynamic scenes. Coded Aperture Snapshot Spectral Imaging (CASSI) is a cutting-edge technique for spectral imaging that allows for the rapid acquisition of spectral images of dynamic scenes from a single exposure. It consists of "encoding dimension-reduction collection" of high-dimensional spectral images and "decoding dimension-increase reconstruction" of low-dimensional measurements. Early research on CASSI primarily focused on the "encoding dimension-reduction collection" stage, aiming to enhance the effectiveness of image encoding through physical system design, including the design of coded aperture and dual-camera system. At present, the physical system for the "encoding dimension-reduction collection" stage has become fixed. The "decoding dimension-increase reconstruction" stage plays a crucial role in determining the quality and efficiency of spectral imaging. This paper presents a comprehensive overview of CASSI reconstruction algorithms, which aims to provide readers with a detailed understanding of the inner workings and intricacies of the various algorithms. First, we introduce the physical system and forward model of CASSI. providing a detailed description of the components and hardware parameters of the physical system and deriving the mathematical expression of the CASSI forward model. Second, we outline the characteristics and challenges of CASSI reconstruction, which mainly contain the forward model, prior representation model, algorithm flexibility, algorithm complexity, and real-world datasets. Next, we summarize the current research status of reconstruction algorithms, including optimization-based and learning-based reconstruction algorithms. The optimization-based reconstruction algorithms employ convex optimization models to address the challenging linear inverse problems effectively. These algorithms apply crafted prior representation models, including but not limited to smoothness, sparsity. and low-rank, to tackle the inverse problem's inherent ill-posedness. The learning-based reconstruction algorithms take a different data-driven approach to establishing prior representation models. These algorithms leverage the power of deep learning frameworks, such as end-to-end networks, deep unfolding, and plug-and-play, to effectively solve the reconstruction problem. With the capabilities of deep learning, these algorithms can learn and fit the underlying patterns and structures within the data, leading to enhanced reconstruction performance. By contrasting the optimization-based and learning-based methods, we comprehensively understand the diverse methodologies of CASSI reconstruction to explore the inner workings and potential benefits and limitations. Furthermore, a thorough comparison is conducted utilizing various evaluation metrics to assess mainstream algorithms' reconstruction quality and computational efficiency. These metrics include peak signal-to-noise ratio, structural similarity, and spectral angle mapping for evaluating the reconstruction quality. In addition, model parameter count and floating-point operations are utilized to measure the computational efficiency. Finally, the shortcomings of existing work and future research trends are discussed. The unresolved pain points in the current field are identified, and potential research directions are highlighted, providing valuable insights for further innovation and advancement.