Abstract Super-resolution (SR), aiming to super-resolve degraded low-resolution image to
recover the corresponding high-resolution counterpart, is an important and challenging task …

With the rapid advancements in the field of autonomous driving, the need for faster and more
accurate object detection frameworks has become a necessity. Many recent deep learning …

With the accelerated development of artificial intelligence, remote-sensing image
technologies have gained widespread attention in smart cities. In recent years, remote …

Due to the miniature scale and limited identifiable features, small objects pose a significant
challenge in detection. Improving the accuracy of small object detection is a momentous …

Introduction: Object detection in remotely sensed satellite images is critical to socio-
economic, bio-physical, and environmental monitoring, necessary for the prevention of …

Small object detection in large-scale remote sensing images (RSIs) is crucial for military and
civil applications, but it remains challenging. Since small objects occupy few pixels, their …

Having a tiny scale and few identifiable features, small objects are particularly difficult to
detect, especially when the image resolution is not high. Further, large-scale variation …

Single-image super-resolution (SISR) for high-resolution (HR) remote sensing image (RSI)
acquisition is becoming increasingly valuable and important, and convolutional neural …

Recently, single image super-resolution (SISR) has been an activate research topic for
decades, which is a classical fundamental problem in low-level computer vision tasks. With …

The lack of vehicle feature information and the limited number of pixels in high-definition
remote-sensing images causes difficulties in vehicle detection. This paper proposes U …