Hyperspectral compressed sensing (HCS) is a new imaging method that effectively reduces the power consumption of data acquisition. In this paper, we present a novel HCS algorithm by incorporating spatial-spectral hybrid compressed sensing, followed by a reconstruction based on spectral unmixing. At the sampling stage, the measurements are acquired by a spatial-spectral hybrid compressive sampling scheme to preserve the necessary information for the following spectral unmixing, where spatial compressive sampling mainly retains the endmember information, and spectral compressive sampling mainly retains the abundance information. Due to the limitations of the traditional linear mixed model (LMM), an improved mixed model is proposed for HCS reconstruction, which considers spectral variability, nonlinear mixing and other factors. At the reconstruction stage, based on the improved mixed model, semi-nonnegative matrix factorization (semi-NMF) is introduced to realize spectral unmixing on the measurements to achieve the final reconstruction by using an alternate iteration manner. The proposed algorithm is tested on real hyperspectral data and the selection of parameters is fully analyzed. Experimental results demonstrate that the proposed algorithm can significantly outperform state-of-the-art HCS algorithms in terms of reconstruction performance.

Hyperspectral compressed sensing (HCS) based on spectral unmixing technique has shown great reconstruction performance. In particular, the linear mixed model (LMM) has been widely used in HCS reconstruction. However, due to the complexity of environmental conditions, instrumental configurations, and material nonlinear mixing effects, LMM cannot accurately represent the hyperspectral images, which limits the improvement of reconstruction quality. In this article, first, by introducing spectral variability, nonlinear mixing, and residuals, a multitype mixed model (MMM) is proposed to establish a more accurate hyperspectral image model. Then, a novel MMM-based HCS is proposed, which performs spectral compressed sampling at the sampling stage only, and at the reconstruction stage, by using spectral unmixing, an MMM-based HCS super-resolution reconstruction algorithm from spectral compressed sensing data is developed, and the alternating direction multiplier method is employed to estimate each component of the MMM, furthermore, reasonable prior knowledge of each component is introduced to improve the estimation accuracy. Experimental results on hyperspectral datasets demonstrate that the proposed model outperforms those state-of-the-art methods based on the LMM in terms of HCS reconstruction quality.

The ever-increasing resolution puts tremendous pressure to the onboard hyperspectral imaging system. Compressed sensing technology is one of the important ways to solve this problem. Distributed compressed sensing was proposed to exploit both intra- and inter-correlation structures of hyperspectral images. However, the implementation method of distributed compressed sampling has not been reported, and the joint sparsity reconstruction algorithm cannot achieve excellent image reconstruction performance. In this paper, a distributed compressed sampling strategy inspired by distributed compressed video sensing and optical implementation model are proposed to collect compressed hyperspectral data. In the image reconstruction process, we discard the direct application of the joint sparsity constraint on the data itself. Instead, we explore the estimation method of abundance and endmember with the help of the existing spectral library. Then, the images are recovered by applying the linear mixing model of hyperspectral. The comparison experiments of various schemes show that the proposed compressed sensing scheme has an obvious advantage in reconstruction performance under the low sampling rate. The proposed compressed sensing scheme has great potential in a high-compression ratio onboard hyperspectral imaging system.

Compressed sensing is one of the key technologies to reduce the volume of hyperspectral image for real-time storage and transmission. Reconstruction based on spectral unmixing show tremendous potential in hyperspectral compressed sensing compared with other conventional algorithms that directly reconstruct images. In this paper, a joint spatial-spectral joint compressed sensing scheme is proposed. In this scheme, compressed hyperspectral data are collected by spatial-spectral hybrid compressed sampling. As for the reconstruction, an objective function is developed by introducing the fidelity constraints of spatial and spectral measurements and the row sparsity constraint of abundance that guarantee the precise reconstruction of hyperspectral images and obtain endmembers and abundances as by-products accurately. An augmented Lagrangian type algorithm is meticulously elaborated to solve the above optimization problem. Extensive experimental results on several real hyperspectral datasets indicate that the proposed approach can achieve a reconstruction accuracy higher than those of other state-of-the-art methods. The efficiency and feasibility of the proposed scheme give it great potential in hyperspectral compressed sensing.

Colorectal cancer (CRC) remains one of the most prevalent and life-threatening diseases globally. In the development of computer-aided diagnosis systems for CRC, accurate polyp segmentation plays a pivotal role. Despite technological advancements, the segmentation of diminutive and multiple polyps continues to remain significant challenges. To address these issues, we introduce an auxiliary attention module (AAM), which leverages a channel attention module, a spatial attention module, and a feature enhancement module to simultaneously capture both global and local features. This parallel learning approach enhances the ability of model to focus on global details and localized regions, particularly for small and flat polyps. Additionally, we propose an area adaptive loss (AAL) to mitigate the under-segmentation of diminutive and multiple polyps. The AAL dynamically adjusts loss weights based on the size and quantity of polyps, prioritizing smaller and more hard samples during training. This adaptive mechanism ensures that the model is better equipped to handle imbalanced and difficult cases, thereby improving segmentation accuracy. Our proposed framework, DoubleAANet, integrates the AAM and AAL to emphasize difficult examples and localized information, enhancing the robustness and precision. To validate the effectiveness and generalization capability of our approach, we conduct extensive experiments on three diverse datasets—Kvasir-SEG, CVC-ClinicDB, and Kvasir-Sessile—as well as a cross-dataset evaluation. The results demonstrate state-of-the-art performance, with our method achieving 86.65% mIoU and 92.17% mDSC on Kvasir-SEG, 90.00% mIoU and 94.58% mDSC on CVC-ClinicDB, and 77.22% mIoU and 82.39% mDSC on Kvasir-Sessile. Notably, on the Kvasir-Sessile dataset, which contains small, flat, and diminutive polyps, our approach achieves a 12.17% improvement in recall, effectively addressing the issue of missed polyps while maintaining a superior balance between recall and precision. The cross-dataset evaluation further underscores the generalization ability of DoubleAANet, achieving 76.90% mIoU and 84.77% mDSC. Comprehensive experimental results confirm that our proposed method outperforms most existing state-of-the-art segmentation techniques, offering a significant advancement in polyp segmentation for CRC diagnosis. Source code will be available at https://github.com/feixiangdu/DoubleAANet.

In recent years, hyperspectral images (HSIs) have been widely used in the fields of ground classification, environmental monitoring, military reconnaissance, and so on, since they contains abundant spatial and spectral information of imaging objects. However, with the continuous improvement of both spatial and spectral resolutions of imaging spectrometer, the amount of acquired hyperspectral data increases exponentially, which brings great challenges to the data storage and transmission. Compressive sensing (CS) technique integrates data acquisition and compression, which collect the sparse or compressible signal at a much lower sampling rate than Nyquist sampling theorem and reconstruct the original signal with a higher probability.¹ At present, CS has been successfully applied in HSI compression.^{2 – 6} Hyperspectral CS only captures a small number of sampling data of the original HSI by using incoherent measurement for reconstruction. Compared with traditional compression methods, hyperspectral CS significantly reduces the amount of data that needs to be collected; moreover, it also reduces the resource consumption of imaging. Therefore, hyperspectral CS has attracted remarkable attention in the field of hyperspectral remote sensing.