An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution

arXiv:2405.04595v1 Announce Type: cross
Abstract: In recent years, convolutional neural networks (CNNs) have achieved remarkable advancement in the field of remote sensing image super-resolution due to the complexity and variability of textures and structures in remote sensing images (RSIs), which often repeat in the same images but differ across others. Current deep learning-based super-resolution models focus less on high-frequency features, which leads to suboptimal performance in capturing contours, textures, and spatial information. State-of-the-art CNN-based methods now focus on the feature …

abstract advanced advancement arxiv cnns complexity convolutional convolutional neural networks cs.cv eess.iv extraction features image images networks neural networks repeat resolution sensing type