The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …

Super-resolution (SR) reconstruction is a hot topic in medical image processing. SR implies
reconstructing corresponding high-resolution (HR) images from observed low-resolution …

Boosted by the progress in deep learning, Single Image Super-Resolution (SISR) has
gained a lot of interest in the remote sensing community, who sees it as an opportunity to …

Modern Earth observation satellites capture multi-exposure bursts of push-frame images that
can be super-resolved via computational means. In this work, we propose a super-resolution …

Detecting small and densely distributed (SDD) objects in large-size images is quite
challenging due to the facts that directly inputting such images to the detection network …

In this paper, we propose an original algorithm allowing the registration of multimodal
images. Indeed, the large and nonlinear intensity changes between different modalities …

This paper proposes a Fusion and Recalibration Network (FRN) for low-light image
enhancement. Firstly, The proposed method generates multi-exposure images from a single …

摘要针对多光谱遥感图像超分辨率重建易受噪声和色差影响的问题, 提出了暗通道与交叉通道多
先验联合的多光谱超分辨率算法. 首先, 在传统全变分先验的基础上, 引入了暗通道先验与交叉 …

Super-resolution (SR), a technique to increase the resolution of images, is a pre-processing
step in the pipelines of applications of Earth observation (EO) data. The manual design and …

Image segmentation is a crucial step in remote sensing application, as it breaks down a
larger image into smaller chunks, which contains useful information. Although there are …