RIFLE: Removal of Image Flicker-Banding via Latent Diffusion Enhancement

Capturing screens is common, but photos of emissive displays are often influenced by \emph{flicker-banding} (FB), some alternating bright--dark stripes due to temporal aliasing between a camera's rolling-shutter readout and display's brightness modulation. Unlike moir\'e degradation, FB remains underexplored despite its frequent and severe impact on readability and perceived quality. We formulate FB removal as a dedicated restoration task and introduce Removal of Image Flicker-B}anding via Latent Diffusion Enhancement, RIFLE, a diffusion-based framework designed to remove FB with fine details. We propose Banding-Suppressed High-Frequency Prior (BSHP), which combines gradient-based high-frequency localization with a smooth structural support map to build a compact restoration prior, and injects it into the restoration backbone via multi-stage FiLM modulation. Moreover, Masked Loss (ML) is proposed to concentrate supervision on banded regions without sacrificing global fidelity. To overcome data scarcity, we provide a simulation pipeline synthesizing FB in the luminance domain with stochastic jitter in banding angle, spacing, and width. Feathered boundaries and sensor noise are also applied for a more realistic simulation. For evaluation, we collect a paired real-world FB dataset with pixel-aligned banding-free references captured via long exposure. Across quantitative metrics and visual comparisons on our real-world dataset, RIFLE consistently outperforms recent image reconstruction baselines. To the best of our knowledge, it is the first work to research the simulation and removal of FB. Our dataset and code will be released soon.