FaithEIR combines learnable reversible latent transformations, an adaptive high-frequency detail prior, and semantic conditioning to outperform prior methods in fidelity and perceptual quality for extreme image rescaling.
Ntire 2017 challenge on single image super- resolution: Dataset and study
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
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ARDIS enables arbitrary-resolution deep image steganography via frequency decoupling in hiding and latent-guided implicit reconstruction for blind recovery.
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Faithful Extreme Image Rescaling with Learnable Reversible Transformation and Semantic Priors
FaithEIR combines learnable reversible latent transformations, an adaptive high-frequency detail prior, and semantic conditioning to outperform prior methods in fidelity and perceptual quality for extreme image rescaling.
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Breaking the Resolution Barrier: Arbitrary-resolution Deep Image Steganography Framework
ARDIS enables arbitrary-resolution deep image steganography via frequency decoupling in hiding and latent-guided implicit reconstruction for blind recovery.