The advancements in image super-resolution technology have led to its widespread use in remote sensing applications. However, there is currently a lack of a general solution for the reconstruction of satellite images at arbitrary resolutions. The exi...
Remote sensing image super-resolution aims to reconstruct high-resolution images from low-resolution observations and is important for image interpretation. Existing fixed-scale methods achieve good performance at predefined integer scales, but their...
Recently, several arbitrary-scale models have been proposed for single-image super-resolution. Furthermore, the importance of arbitrary-scale single image super-resolution is emphasized for applications such as satellite image processing, HR display,...
The existing single image super-resolution (SISR) methods that consider integer scale factors (X2, X3, X4, and X8), have been developed well, but SISR methods with arbitrary scale factors (X1.3, X2.5, and X3.7) have gradually gained attention recentl...
High-resolution images of non-cooperative spacecraft are essential for on-board autonomous operations. Hardware bandwidth limits and continuously changing observation distances mean that a practical super-resolution (SR) system must handle arbitrary,...
Traditional single image super-resolution (SISR) methods, which focus on integer scale super-resolution, often require separate training for each scale factor, leading to increased computational resource consumption. In this paper, we propose MambaSR...
Depth sensing has improved rapidly in recent years, which allows for structural information to be utilized in various applications, such as virtual reality, scene and object recognition, view synthesis, and 3D reconstruction. Due to the limitations o...
Planetary remote sensing super-resolution aims to enhance the spatial resolution and fine details from low-resolution images. In practice, planetary remote sensing is inherently constrained by sensor payload limitations and communication bandwidth, r...
This work aims to develop a three-dimensional (3D) super-resolution (SR) network that would perform arbitrary-scale 3D whole-heart (WH) magnetic resonance imaging (MRI) super-resolution, while maintaining fine image details. One-hundred-twenty 3D WH...
Deep convolutional neural networks with strong expressive ability have achieved impressive performances in single-image super-resolution algorithms. However, excessive convolutions usually consume high computational cost, which limits the application...
Remote sensing images often have limited resolution, which can hinder their effectiveness in various applications. Super-resolution techniques can enhance the resolution of remote sensing images, and arbitrary resolution super-resolution techniques p...
The single-image super-resolution task benefits has a wide range of application scenarios, so has long been a hotspot in the field of computer vision. However, designing a continuous-scale super-resolution algorithm with excellent performance is stil...
High-resolution planetary remote sensing imagery provides detailed information for geomorphological and topographic analyses. However, acquiring such imagery is constrained by limited deep-space communication bandwidth and challenging imaging environ...
Accurate aircraft detection in remote sensing imagery is critical for aerospace surveillance, military reconnaissance, and aviation security but remains fundamentally challenged by extreme scale variations, arbitrary orientations, and dense spatial c...
Super-resolution (SR) of remote sensing images (RSIs) is essential for advanced image analysis, yet its progress is challenged by the ill-posed nature of SR and the geometric displacement errors commonly found in paired low-resolution (LR) and high-r...
In recent years, deep-learning-based super-resolution (SR) methods have obtained impressive performance gains on synthetic clean datasets, but they fail to perform well in real-world scenarios due to insufficient real-world training data. To tackle t...