Image Demoireing with Learnable Bandpass Filters

Abstract

Image demoireing is a multi-faceted image restoration task involving both texture and color restoration. In this paper, we propose a novel multiscale bandpass convolutional neural network (MBCNN) to address this problem. As an end-to-end solution, MBCNN respectively solves the two sub-problems. For texture restoration, we propose a learnable bandpass filter (LBF) to learn the frequency prior for moire texture removal. For color restoration, we propose a two-step tone mapping strategy, which first applies a global tone mapping to correct for a global color shift, and then performs local fine tuning of the color per pixel. Through an ablation study, we demonstrate the effectiveness of the different components of MBCNN. Experimental results on two public datasets show that our method outperforms state-ofthe-art methods by a large margin (more than 2dB in terms of PSNR).

A Moiré Removal Method Based on Peak Filtering and Image Enhancement

“…To highlight the advantages of the proposed method, we compared it with state-ofthe-art methods, including DMCNN [25], HRDN [26], MBCNN [27], MopNet [31], and EEUCN [33]. The results are shown in Figure 12, which shows that the proposed method achieved better performance compared to these methods.…”

“…HRDN can process lower-resolution images while maintaining the full resolution of the inputs and handle different frequencies at different scales. Zheng et al [27] extracted features of moiré components in the image frequency domain and proposed MBCNN (Multiscale Bandpass CNN) to perform moiré removal while preserving the colour and texture recovery. Recently, GAN-based demoiré methods [33][34][35] have also been studied, which can be trained without paired (clear and moiré image pair) datasets.…”

“…HRDN [26] did not sufficiently extract features at each scale, resulting in the unsatisfactory removal of moiré patterns from images containing complex moiré patterns. MBCNN [27] extracted the frequency-domain features of the image and analysed and removed the moiré component in the frequency domain. However, it was relatively ineffective at addressing textual images with heavy moiré.…”

“…Yang et al [26] built a highresolution reconstruction model with four branches. Zheng et al [27] utilised a two-step tone-mapping strategy for colour restoration to fine-tune the colour of each pixel. However, some complex textures such as text are also removed during the demoiré procedure, which seriously limits its applicability in practical scenarios.…”