Yeh Chia-Hung, Lo Chen, He Cheng-Han
Department of Electrical Engineering, National Taiwan Normal University, Taipei 10610, Taiwan.
Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan.
Sensors (Basel). 2024 Apr 26;24(9):2762. doi: 10.3390/s24092762.
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.
相机传感器与显示器之间的混叠所导致的莫尔条纹会严重降低图像质量。图像去莫尔条纹是一种包括纹理和颜色恢复的多任务图像恢复方法。本文提出了一种用于去除莫尔条纹的基于小波的新型多分支图像去莫尔条纹网络（MBWDN）。利用小波分解将莫尔条纹图像分离成子带图像，并且可以使用两个网络的不同学习策略来实现去莫尔条纹：去莫尔条纹网络（MRN）和细节增强去莫尔条纹网络（DMRN）。MRN从低频图像中去除莫尔条纹，同时保留平滑区域的结构。DMRN同时去除高频莫尔条纹并增强图像中的精细细节。使用小波分解来代替传统的上采样，并且最大池化有效地增加了网络的感受野而不丢失空间信息。通过使用小波变换将莫尔条纹图像分解到不同级别，可以充分保留每个分支的特征学习结果并将其输入到下一个分支；因此，避免了恢复图像中可能出现的失真。由于在特征训练期间对高频和低频图像进行了分离，所提出的两个网络实现了令人印象深刻的去莫尔条纹效果。基于使用公共数据集进行的大量实验，与现有方法相比，所提出的方法在定量和定性方面都显示出良好的去莫尔条纹有效性。
