A hyperspectral image (HSI) is often corrupted by various types of noise during image acquisition, e.g., Gaussian noise, impulse noise, stripes, deadlines, and more. Thus, as a preprocessing step, HSI denoising plays a vital role in many subsequent tasks. Recently, a variety of mixed noise removal approaches have been developed for HSI, and the methods based on spatial–spectral double factor and total variation (DFTV) regularization have achieved comparable performance. Additionally, the nonlocal low-rank tensor model (NLR) is often employed to characterize spatial nonlocal self-similarity (NSS). Generally, fully exploring prior knowledge can improve the denoising performance, but it significantly increases the computational cost when the NSS prior is employed. To solve this problem, this article proposes a novel DFTV-based NLR regularization (DFTVNLR) model for HSI mixed noise removal. The proposed model employs low-rank tensor factorization (LRTF) to characterize the spectral global low-rankness (LR), introduces 2-D and 1-D TV constraints on double-factor to characterize the spatial and spectral local smoothness (LS), respectively. Meanwhile, the NLR is applied to the spatial factor to characterize the NSS. Then, we developed an algorithm based on proximal alternating minimization (PAM) to solve the proposed model effectively. Particularly, we effectively controlled the computational cost from two aspects, namely taking small-sized double factor as regularization object and putting the time-consuming NLR model before the main loop with fewer iterations to solve it independently. Finally, considerable experiments on simulated and real noisy HSI substantiate that the proposed method is superior to the related state-of-the-art methods in balancing the denoising effect and speed.

The modulation transfer function (MTF) is commonly used as an imaging quality criterion reflecting the spatial resolution capability of imaging systems. The modified edge methods based on ISO Standard 12233 are widely used in MTF measurement for various imaging fields with high confidence. However, there are two problems in the existing edge methods which limit the application in remote sensing (RS) field with complicated image quality and usually uncontrollable edge angle: a near-horizontal or near-vertical “small tilt angle straight (STAS)” edge is required, and the MTF measurement results show low robustness and non-uniqueness. In this study, the influence of edge angle, oversampling rate (OSR), region of interest (ROI), edge contrast, and random noise on the edge method accuracy is quantitatively analyzed, and a practical multidirectional MTF measurement edge method is proposed based on the above analysis results. The modified edge method adaptively determines the optimal OSR according to edge angle and combines multiple measurement states, such as multi-ROI extraction and multi-phase binning, to improve the robustness, accuracy, and practicality of the edge method.