Spatial Adaptive Filter Network With Scale-Sharing Convolution for Image Demoiréing

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-09-09 DOI:10.1109/LSP.2024.3451948
Yong Xu;Zhiyu Wei;Ruotao Xu;Zihan Zhou;Zhuliang Yu
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Abstract

Removing moiré patterns is a challenging task as it is a spatially varying degradation that varies in shape, color and scale. Existing image restoration models often rely on static convolutional neural networks (CNNs)-based architectures, and hence potentially suboptimal for addressing the diverse manifestations of moiré patterns across different images and spatial positions. To this end, we propose a spatially adaptive neural network for image demoiréing. This network introduces a dual-branch filter prediction module engineered to predict pixel-wise adaptive filters that can process moiré patterns of varying orientations and color-shift issues. To further tackle the challenge presented by scale variability, a scale-sharing convolution module is proposed, utilizing pixel-wise adaptive filters with multiple dilations to handle moiré patterns of different sizes but similar shapes effectively. Upon extensive evaluations of three benchmark datasets, our model consistently outperforms existing methods, yielding a PSNR improvement of over 0.37dB across all evaluated datasets and providing additional benefits in terms of model size.
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利用规模共享卷积的空间自适应滤波网络进行图像演示
消除摩尔纹是一项极具挑战性的任务,因为摩尔纹是一种在形状、颜色和比例上都各不相同的空间退化现象。现有的图像修复模型通常依赖于基于静态卷积神经网络(CNN)的架构,因此可能无法很好地解决摩尔纹在不同图像和空间位置上的各种表现形式。为此,我们提出了一种空间自适应神经网络,用于图像纹理分析。该网络引入了一个双分支滤波器预测模块,旨在预测像素自适应滤波器,以处理不同方向和色移问题的摩尔纹图案。为了进一步应对尺度变化带来的挑战,我们提出了一个尺度共享卷积模块,利用像素自适应滤波器的多重扩张来有效处理大小不同但形状相似的摩尔纹图案。经过对三个基准数据集的广泛评估,我们的模型始终优于现有方法,在所有评估数据集上的 PSNR 均提高了 0.37dB 以上,并在模型大小方面提供了额外的优势。
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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