No abstract

No abstract

Hyperspectral imaging has been widely used for agriculture, astronomy, surveillance, and so on. However, hyperspectral imaging usually suffers from low‐spatial resolution, due to the limited photons in individual bands. Recently, more hyperspectral image super‐resolution methods have been developed by fusing the low‐resolution hyperspectral image and high‐resolution RGB image, but most of them did not consider the misalignment between two input images. In this study, the authors present an effective method to restore a high‐resolution hyperspectral image from the misaligned low‐resolution hyperspectral image and high‐resolution RGB image, which exploits spectral and spatial correlation in hyperspectral and RGB images. Specifically, they employ the spectral sparsity to restore the high‐resolution hyperspectral image on the misaligned part, and then simultaneously employ spectral and spatial structure correlation to restore the high‐resolution hyperspectral image on the aligned area, which can be fused to obtain the high‐quality hyperspectral image restoration under a misaligned hybrid camera system. Experimental results show that the proposed method outperforms the state‐of‐the‐art hyperspectral image super‐resolution methods under a misaligned hybrid camera system in terms of both objective metric and subjective visual quality.

No abstract

Mutual information can indicate the degradation severity of natural images by capturing images' local structural properties. In this paper, we analyze the dependence between neighboring pixels and propose a no reference image quality assessment method based on mutual information in wavelet domain. The proposed image quality assessment (IQA) method, named MIQA-II, computes mutual information between neighboring pixels in the same subband, across different orientations and scales as features. A quadratic function is used to fit the mutual information values along a distance and the features are then transformed to a final predicted quality score through a two-step framework. MIQA-II is tested on the LIVE IQA database. The experimental results show that MIQA-II has a competitive subjective relevance performance and acceptable time consumption. In addition, MIQA-II still achieves good performance with a small training set.