arXiv:2011.07017
공식 코드 · Papers with Code
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
원문 초록
The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night. The gap can be filled by employing available infra-red observations to generate visible images. This work presents how deep learning can be applied successfully to create those images by using U-Net based architectures. The proposed methods show promising results, achieving a structural similarity index (SSIM) up to 86\% on an independent test set and providing visually convincing output images, generated from infra-red observations.
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Residual Prior-driven Frequency-aware Network for Image Fusion
Cloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions
BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation
SkipVAR: Accelerating Visual Autoregressive Modeling via Adaptive Frequency-Aware Skipping
MagCache: Fast Video Generation with Magnitude-Aware Cache
SSIMBaD: Sigma Scaling with SSIM-Guided Balanced Diffusion for AnimeFace Colorization
토론
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