EmambaIR is a visual state space model with cross-modal top-k sparse attention and gated SSM components that outperforms prior CNN and ViT methods on event-guided deblurring, deraining, and HDR reconstruction while reducing memory and compute costs.
Accurate image restora- tion with attention retractable transformer
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SAT introduces density and isolation-based token aggregation to enable efficient global attention in super-resolution transformers, claiming up to 0.22 dB PSNR gain and 27% FLOP reduction over PFT.
Nano Banana 2 delivers competitive perceptual quality on image restoration but produces over-enhanced results that diverge from input fidelity in ways standard metrics miss.
citing papers explorer
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EmambaIR: Efficient Visual State Space Model for Event-guided Image Reconstruction
EmambaIR is a visual state space model with cross-modal top-k sparse attention and gated SSM components that outperforms prior CNN and ViT methods on event-guided deblurring, deraining, and HDR reconstruction while reducing memory and compute costs.
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SAT: Selective Aggregation Transformer for Image Super-Resolution
SAT introduces density and isolation-based token aggregation to enable efficient global attention in super-resolution transformers, claiming up to 0.22 dB PSNR gain and 27% FLOP reduction over PFT.
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Can Nano Banana 2 Replace Traditional Image Restoration Models? An Evaluation of Its Performance on Image Restoration Tasks
Nano Banana 2 delivers competitive perceptual quality on image restoration but produces over-enhanced results that diverge from input fidelity in ways standard metrics miss.