# Multibranch Wavelet-Based Network for Image Demoiréing

## TL;DR

This paper introduces a new image processing method to remove moiré patterns using a wavelet-based network that improves image quality by handling different frequency components.

## Contribution

The novel MBWDN network uses wavelet decomposition and dual learning strategies to effectively remove moiré patterns while preserving image details.

## Key findings

- MBWDN outperforms existing methods in both quantitative and qualitative moiré removal.
- Wavelet decomposition preserves features across different frequency levels, avoiding image distortions.
- The MRN and DMRN networks effectively handle low- and high-frequency moiré patterns respectively.

## Abstract

Moiré patterns caused by aliasing between the camera’s sensor and the monitor can severely degrade image quality. Image demoiréing is a multi-task image restoration method that includes texture and color restoration. This paper proposes a new multibranch wavelet-based image demoiréing network (MBWDN) for moiré pattern removal. Moiré images are separated into sub-band images using wavelet decomposition, and demoiréing can be achieved using the different learning strategies of two networks: moiré removal network (MRN) and detail-enhanced moiré removal network (DMRN). MRN removes moiré patterns from low-frequency images while preserving the structure of smooth areas. DMRN simultaneously removes high-frequency moiré patterns and enhances fine details in images. Wavelet decomposition is used to replace traditional upsampling, and max pooling effectively increases the receptive field of the network without losing the spatial information. Through decomposing the moiré image into different levels using wavelet transform, the feature learning results of each branch can be fully preserved and fed into the next branch; therefore, possible distortions in the recovered image are avoided. Thanks to the separation of high- and low-frequency images during feature training, the proposed two networks achieve impressive moiré removal effects. Based on extensive experiments conducted using public datasets, the proposed method shows good demoiréing validity both quantitatively and qualitatively when compared with the state-of-the-art approaches.