FlickerDiff: Uncertainty-Controlled One-Step Diffusion for Flicker Removal

Spatially non-uniform degradation makes it difficult for image restoration models to balance fidelity and generative recovery. Single-image flicker removal is a representative example of this problem. Flicker bands can severely attenuate text, edges, and object structures in some regions while leaving nearby content largely intact. Existing diffusion-based restoration methods often apply a uniform noise level or restoration strength across the image. They may therefore under-recover severely degraded regions or unnecessarily modify reliable content. We propose FlickerDiff, a one-step pixel-space diffusion model that adjusts this balance according to local reliability. A lightweight auxiliary network predicts an uncertainty map, which determines the spatially varying transport variance in the flow-matching endpoint distribution. Reliable regions follow narrow paths anchored to the observation, while severely degraded regions receive a larger stochastic transport budget and can rely more strongly on the pretrained generative prior. The same uncertainty map is injected into the generator through token-aligned FiLM modulation. We adapt a pretrained 8B-parameter pixel-space Diffusion Transformer in its velocity-prediction space to build the one-step generator. Adversarial detail supervision and a lightweight post-fusion refiner further improve perceptual quality and output fidelity. Experiments show that FlickerDiff achieves the strongest reconstruction fidelity and perceptual quality among the compared methods. At inference, the full pipeline consists of uncertainty estimation, a single Pixel-DiT forward pass, and lightweight post-fusion refinement, without iterative diffusion sampling.