MoCHA-former: Moiré-conditioned hybrid adaptive transformer for video demoiréing

Recent advances in portable imaging have made camera-based screen capture ubiquitous. Unfortunately, frequency aliasing between the camera’s color filter array (CFA) and the display’s sub-pixels induces moiré patterns that severely degrade captured photos and videos. Although various demoiréing models have been proposed to remove such moiré patterns, these approaches still suffer from several limitations: (i) spatially varying artifact strength within a frame, (ii) large-scale and globally spreading structures, (iii) channel-dependent statistics and (iv) rapid temporal fluctuations across frames. We address these issues with the Moiré Conditioned Hybrid Adaptive Transformer (MoCHA-former), which comprises two key components: Decoupled Moiré-Adaptive Demoiréing (DMAD) and Spatio-Temporal Adaptive Demoiréing (STAD). DMAD separates moiré and content via a Moiré Decoupling Block (MDB) and a Decoupling Distillation Block Block (DDB), then produces moiré-adaptive features using a Moiré-Adaptive Cross-Attention Block (MCB) for targeted restoration. STAD proposes a Spatio-temporal Frequency-aware Transformer Block (TransBlock) with window attention to capture large-scale structures, and a Fourier-domain channel attention to model channel dependence in RAW frames. To ensure temporal consistency, MoCHA-former performs implicit frame alignment without any explicit alignment module. We analyze moiré characteristics through qualitative and quantitative studies, and evaluate on two video datasets covering RAW and sRGB domains. MoCHA-former consistently surpasses prior methods across PSNR, SSIM, and LPIPS. • Analyze moiré characteristics via qualitative and quantitative evaluations. • Propose DMAD to generate moiré-adaptive features for effective demoiréing. • Introduce STAD to capture spatio-temporal cues and channel-dependent moiré characteristics.