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Super Resolution for Satellite Imagery Applying super resolution strategies to sattelite imagery
The main features of warrengreen/srcnn are: Geospatial Machine Learning, Super Resolution.
Projects with overlapping indexed features include: spipm/depixelization_poc — This project is an AI upscaling framework and deep learning image restorer designed to estimate original source pixels… torchgeo/torchgeo — TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and… paddlepaddle/paddlegan — PaddleGAN is a generative AI framework and deep learning computer vision library built on the PaddlePaddle framework.… ar0kim/fidesr. avanetten/simrdwn — The Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) codebase combines some of the… arctichare105/s3diff.
This project is an AI upscaling framework and deep learning image restorer designed to estimate original source pixels from low-resolution inputs. It functions as a super-resolution reconstruction system that transforms pixelated images into high-resolution versions by restoring high-frequency details and sharpening edges. The system utilizes a convolutional neural network pipeline to analyze pixel data and perform digital image restoration. It employs pixel-shuffle upsampling to rearrange channel dimensions into spatial dimensions, which increases resolution while reducing checkerboard artif
TorchGeo is a PyTorch library designed for deep learning on geospatial data, providing a framework for building and training neural networks for tasks such as semantic segmentation, object detection, and change detection. It serves as a comprehensive pipeline for remote sensing, featuring specialized dataset loaders and multispectral image preprocessing tools. The library is distinguished by a dedicated remote sensing model zoo and extensive support for transfer learning, allowing users to integrate pre-trained weights optimized for specific satellite sensors. It also includes support for sel
PaddleGAN is a generative AI framework and deep learning computer vision library built on the PaddlePaddle framework. It serves as a toolkit for image and video synthesis, providing a collection of generative adversarial network implementations for creating synthetic visual content. The library focuses on advanced synthesis capabilities, including the generation of talking heads through lip motion synchronization and the creation of synthetic videos via motion transfer from driving sequences. It provides tools for domain-to-domain translation, allowing for image style transfer and the transfo