Multi-mode Tensor Train Factorization with Spatial-spectral Regularization for Remote Sensing Images Recovery

RFVTM: A Recovery and Filtering Vertex Trichotomy Matching for Remote Sensing Image Registration

Reliable feature point matching is a vital yet challenging process in feature-based image registration. In this paper,a robust feature point matching algorithm called Recovery and Filtering Vertex Trichotomy Matching (RFVTM) is proposed to remove outliers and retain sufficient inliers for remote sensing images. A novel affine invariant descriptor called vertex trichotomy descriptor is proposed on the basis of that geometrical relations between any of vertices and lines are preserved after affine transformations, which is constructed by mapping each vertex into trichotomy sets. The outlier remo…

Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network

Optical remote sensing imagery has been widely used in many fields due to its high resolution and stable geometric properties. However, remote sensing imagery is inevitably affected by climate, especially clouds. Removing the cloud in the high-resolution remote sensing satellite image is an indispensable pre-processing step before analyzing it. For the sake of large-scale training data, neural networks have been successful in many image processing tasks, but the use of neural networks to remove cloud in remote sensing imagery is still relatively small. We adopt generative adversarial network t…

Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery

This paper proposes a non-data-driven deep neural network for spectral image recovery problems such as denoising, single hyperspectral image super-resolution, and compressive spectral imaging reconstruction. Unlike previous methods, the proposed approach, dubbed Mixture-Net, implicitly learns the prior information through the network. Mixture-Net consists of a deep generative model whose layers are inspired by the linear and non-linear low-rank mixture models, where the recovered image is composed of a weighted sum between the linear and non-linear decomposition. Mixture-Net also provides a lo…

Coherent synthetic aperture imaging for visible remote sensing via reflective Fourier ptychography

Synthetic aperture radar (SAR) can measure the phase with antenna and microwave, which cannot be directly extended to visible light imaging due to phase lost. In this letter, we reported an active remote sensing with visible light via reflective Fourier ptychography (FP), termed coherent synthetic aperture imaging (CSAI), achieving high resolution, wide field-of-view (FOV) and phase recovery. A proof-of-concept experiment was reported with laser scanning and a collimator for the infinite object. Both smooth and rough objects are tested, and the spatial resolution increased from 15.6 um to 3.48…

Low Rank Quaternion Matrix Recovery via Logarithmic Approximation

In color image processing, image completion aims to restore missing entries from the incomplete observation image. Recently, great progress has been made in achieving completion by approximately solving the rank minimization problem. In this paper, we utilize a novel quaternion matrix logarithmic norm to approximate rank under the quaternion matrix framework. From one side, unlike the traditional matrix completion method that handles RGB channels separately, the quaternion-based method is able to avoid destroying the structure of images via putting the color image in a pure quaternion matrix. …

HDMba：基于状态空间模型的高光谱遥感图像去雾

HDMba: Hyperspectral Remote Sensing Imagery Dehazing with State Space Model

本文提出首个基于Mamba的高光谱图像去雾网络HDMba，通过窗口选择性扫描模块和残差DehazeMamba块联合建模局部与全局光谱-空间信息，在Gaofen-5数据集上取得最优去雾性能。