EMBER: Rekindling Old Photo Restoration from Early Trajectories and No-Reference Feedback

A faded photograph can be the last visible trace of a person or moment that no longer exists, but restoring it is difficult without clean references or reliable damage masks. Our approach begins with two observations about a pretrained image editor. Its first two denoising steps produce a spatial edit-response signal associated with the regions where the final output later differs from the input. No-reference policy optimization also amplifies restoration and colorization responses that supervised fine-tuning does not consistently express. Based on these observations, we introduce EMBER, which combines a Gated 1–2 Step Edit-Response Field (G12) with no-reference group-relative policy optimization (GRPO) on real old photographs. G12 freezes the early response into mask-free rotary-phase modulation, while GRPO adapts the editor using a reward with no explicit color statistic. Across GGT-100K Old Photo and RealOld-74, EMBER performs best or remains competitive on most no-reference metrics not used as training rewards. In a blinded study, 31 participants completed 1,018 valid image-level evaluations across 48 photographs, selecting their two preferred results in each evaluation. Across all seven methods, EMBER achieved the highest Top-2 inclusion rate at 58.3%. EMBER restores these photographs in the form in which they often survive: as a single damaged image, without clean targets during policy optimization or external masks at deployment. Code is available at https://anonymous.4open.science/r/artifact-af5213a/.