Low-light image enhancement is a necessary preprocessing step for target detection and recognition in low-light environment and urgently needed in night vision monitoring, medical imaging, remote sensing imaging and other fields. With the rapid development of machine learning research, machine learning based low-light image enhancement has attracted extensive attention and achieved good results. However, most of the existing machine learning based low-light image enhancement methods rely on the "bright-dark" paired datasets. On the one hand, the construction of the paired dataset has a high cost, which is not conducive to the promotion and practical application. On the other hand, in practical problems, we can usually get partly-paired images with similar background, and there are a lot of shared information between these images. Taking full advantage of this shared information is also conducive to further improve the efficiency of learning methods. This paper focuses on the low-light image enhancement model based on Retinex theory. By mining and modeling the shared prior between partly-paired images of the same scene, and coupling with the existing machine learning methods based on paired dataset training, a Retinex model for partly-paired low-light image enhancement method with learned prior is proposed. Experiments demonstrate that the proposed method can recover more details and richer colors in visual effects, and can improve the numerical results by up to 20%.
In hyperspectral image, the variation of endmember may significantly alter the signature of corresponding endmember, which influences the detection of anomaly target. In order to distinguish the endmember variability and outlier effectively, a Bayesian anomaly detection being considered the endmember variability unmixing is proposed. The parameters priors are built according to the perturbed linear mixing model. At the same time, outliers usually have high correlations in the spatial domain. So as background. Moreover, the anomaly prior is developed by combining the nonlocal self-similarity and Markov random field priors for a Boolean label map which takes the spatial correlations of the image into consideration. Compared with some classical anomaly detection methods, the experiments on datasets show that the proposed method can effectively improve the detection accuracy and enhance the visual effect.
Hyperspectral super-resolution (HSR) aims at enhancing the spatial resolution of a hyperspectral image (HSI) by fusing with a higher spatial resolution multispectral image (MSI). The shared and complementary spectral-spatial information is crucial to HSR. To fully exploit the spectral-spatial correlation, as well as the intrinsic structure of the HSI and MSI, the coupled block-term decomposition (BTD) of tensor is employed to represent the data. Furthermore, the BTD is regularized by introducing a graph manifold to improve the spatial detail structures of the HR-HSI, which results in a proposed Graph Laplacian-guided Coupled Block-Term Decomposition (GLCBTD) model for the fusion of HSI-MSI. The proposed fusion framework is solved by a block coordinate descent (BCD) algorithm interleaved with the alternating direction method of multipliers (ADMM). Experimental results on both synthetic and real dataset demonstrate that the proposed GLCBTD method is superior to state-of-the-art fusion methods in preserving spatial and spectral details.
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