Efficient JPEG Restoration in the Wavelet Domain via Mean Flows
Latest JPEG restoration systems achieve strong quality with large models, yet often remain too slow and expensive for efficient on-device deployment. We present a 65M-parameter generative restorer that attains the lowest LPIPS at QF 10 and 20 on LIVE-1, Urban100, and DIV2K-val while sustaining 8.05 images/s at $1024\times1024$ on a single RTX 3090, roughly $4.9\times$ the reported throughput of one-step SODiff at one-twentieth of its parameters. Trained from scratch, the model replaces the learned VAE encoder-decoder with an exactly invertible two-level Haar transform, predicts a clean wavelet-domain residual through a rank-enhanced linear-attention DiT that estimates compression severity...
| Authors | Stefan-Alexandru Asandei, Mihai-Alexandru Radu |
|---|---|
| Primary category | eess.IV |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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