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Image Rescaling

Image rescaling is a bidirectional operation, which first downscales high-resolution images to fit various display screens or to be storage- and bandwidth-friendly, and afterward upscales the corresponding low-resolution images to recover the original resolution or the details in the zoom-in images.

Papers

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Showing 1–22 of 22 papers

TitleStatusHype
Geolocating Earth Imagery from ISS: Integrating Machine Learning with Astronaut Photography for Enhanced Geographic MappingCode0
Plug-and-Play Tri-Branch Invertible Block for Image RescalingCode1
Block-Based Multi-Scale Image Rescaling—0
Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared imagesCode1
Timestep-Aware Diffusion Model for Extreme Image Rescaling—0
Invertible Residual Rescaling ModelsCode0
Boundary-aware Decoupled Flow Networks for Realistic Extreme RescalingCode0
Real-time 6K Image Rescaling with Rate-distortion OptimizationCode1
Raising The Limit Of Image Rescaling Using Auxiliary Encoding—0
Self-Asymmetric Invertible Network for Compression-Aware Image RescalingCode1
DINN360: Deformable Invertible Neural Network for Latitude-Aware 360deg Image RescalingCode0
Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images—0
Invertible Rescaling Network and Its ExtensionsCode2
Effective Invertible Arbitrary Image Rescaling—0
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural NetworkCode0
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence—0
Faithful Extreme Rescaling via Generative Prior Reciprocated Invertible RepresentationsCode1
Approaching the Limit of Image Rescaling via Flow Guidance—0
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image RescalingCode1
An Image Forensic Technique Based on JPEG Ghosts—0
Towards a Universal Model for Cross-Dataset Crowd Counting—0
Invertible Image RescalingCode1
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