FDIR: Harmonizing Fidelity and Human-Machine Preference in Lossy Compression Image Restoration
Image restoration quality can be evaluated along three complementary facets: pixel-level fidelity, human perception, and downstream machine preference. However, existing lossy compression restoration methods optimize for at most one of these criteria: fidelity-oriented models often regress toward conditional means and produce over-smoothed outputs, while generative approaches hallucinate plausible but factually incorrect textures that degrade both ground-truth fidelity and downstream task accuracy. To navigate this three-way tradeoff, we propose FDIR, a two-stage architecture that decouples the conflicting demands through complementary inductive biases: Quality-Guided One-Step Flow Matchi...
| Authors | Kuan-Yen Chen, Fang-Yi Su, Philip Chikontwe et al. |
|---|---|
| Primary category | eess.IV |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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