VOSR shows that competitive generative image super-resolution with faithful structures can be achieved by training a diffusion-style model from scratch on visual data alone, using a vision encoder for guidance and a restoration-oriented sampling strategy.
Resshift: Efficient diffusion model for image super-resolution by residual shifting
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MetaSR adaptively orchestrates metadata in a DiT-based generative SR model to deliver up to 1 dB PSNR gains and 50% bitrate savings across diverse content and degradations.
PERCEPT-Net uses motion perceptual loss in a residual U-Net with attention and multi-scale modules to remove MRI motion artifacts more effectively than prior methods on clinical data.
citing papers explorer
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VOSR: A Vision-Only Generative Model for Image Super-Resolution
VOSR shows that competitive generative image super-resolution with faithful structures can be achieved by training a diffusion-style model from scratch on visual data alone, using a vision encoder for guidance and a restoration-oriented sampling strategy.
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MetaSR: Content-Adaptive Metadata Orchestration for Generative Super-Resolution
MetaSR adaptively orchestrates metadata in a DiT-based generative SR model to deliver up to 1 dB PSNR gains and 50% bitrate savings across diverse content and degradations.
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Removing Motion Artifact in MRI by Using a Perceptual Loss Driven Deep Learning Framework
PERCEPT-Net uses motion perceptual loss in a residual U-Net with attention and multi-scale modules to remove MRI motion artifacts more effectively than prior methods on clinical data.