Moir\'eXNet: Adaptive Multi-Scale Demoir\'eing with Linear Attention Test-Time Training and Truncated Flow Matching Prior

TL;DR

This paper presents MoiréXNet, a hybrid demoiréing framework combining linear attention-based test-time training with a flow matching prior to effectively remove moiré patterns from images and videos, especially addressing nonlinear degradation challenges.

Contribution

It introduces a novel hybrid MAP-based approach that integrates efficient linear attention TTT modules with a flow matching prior for improved nonlinear demoiréing performance.

Findings

Enhanced demoiréing results with reduced artifacts.

Effective handling of nonlinear degradation processes.

Improved high-frequency detail restoration.

Abstract

This paper introduces a novel framework for image and video demoir\'eing by integrating Maximum A Posteriori (MAP) estimation with advanced deep learning techniques. Demoir\'eing addresses inherently nonlinear degradation processes, which pose significant challenges for existing methods. Traditional supervised learning approaches either fail to remove moir\'e patterns completely or produce overly smooth results. This stems from constrained model capacity and scarce training data, which inadequately represent the clean image distribution and hinder accurate reconstruction of ground-truth images. While generative models excel in image restoration for linear degradations, they struggle with nonlinear cases such as demoir\'eing and often introduce artifacts. To address these limitations, we propose a hybrid MAP-based framework that integrates two complementary components. The first is a supervised learning model enhanced with efficient linear attention Test-Time Training (TTT) modules, which directly learn nonlinear mappings for RAW-to-sRGB demoir\'eing. The second is a Truncated Flow Matching Prior (TFMP) that further refines the outputs by aligning them with the clean image distribution, effectively restoring high-frequency details and suppressing artifacts. These two components combine the computational efficiency of linear attention with the refinement abilities of generative models, resulting in improved restoration performance.