针对稀疏彩色滤波器阵列的通用深度去马赛克技术

IF 3.4 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Signal Processing-Image Communication Pub Date : 2024-04-29 DOI:10.1016/j.image.2024.117135
Chenyan Bai , Wenxing Qiao , Jia Li
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引用次数: 0

摘要

稀疏滤色镜阵列(CFA)是常用的拜尔滤色镜阵列的潜在替代品,拜尔滤色镜阵列只使用红色(R)、绿色(G)和蓝色(B)像素。在稀疏彩色滤波阵列中,大部分像素是全色(白色)像素,只有一小部分像素是 RGB 像素。稀疏 CFA 具有人类视觉系统的动机和优越的弱光摄影性能。然而,大多数相关的去马赛克方法都高度依赖于合成图像,而且仅限于少数特定的 CFA。本文提出了一种适用于稀疏 CFA 的通用去马赛克方法。我们的方法有两个连续步骤:W 信道恢复和 RGB 信道重建。更具体地说,它首先使用 W 信道内画网络(WCI-Net)来恢复 W 信道。WCI-Net 的第一层执行散点加权插值,这使得网络可以使用各种 CFA。然后,它采用可微引导滤波器,以恢复的 W 信道为参考重建 RGB 信道。可微引导滤波器引入了二进制掩码来指定 RGB 像素的位置。因此,它可以处理任意稀疏的 CFA。此外,它还可以进行端到端训练,因此可以获得卓越的性能,但不会过度拟合合成图像。对干净图像和噪声图像的实验证实了所提出的去马赛克方法的优势。
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Universal deep demosaicking for sparse color filter arrays

Sparse color filter array (CFA) is a potential alternative for the commonly used Bayer CFA, which uses only red (R), green (G), and blue (B) pixels. In sparse CFAs, most pixels are panchromatic (white) ones and only a small percentage of pixels are RGB pixels. Sparse CFAs have the motivation of human visual system and superior low-light photography performance. However, most of the associated demosaicking methods highly depend on synthetic images and are limited to a few specific CFAs. In this paper, we propose a universal demosaicking method for sparse CFAs. Our method has two sequential steps: W-channel recovery and RGB-channel reconstruction. More specifically, it first uses the W channel inpainting network (WCI-Net) to recover the W channel. The first layer of WCI-Net performs the scatter-weighted interpolation, which enables the network to work with various CFAs. Then it employs the differentiable guided filter to reconstruct the RGB channels with the reference of recovered W channel. The differentiable guided filter introduces a binary mask to specify the positions of RGB pixels. So it can handle arbitrary sparse CFAs. Also, it can be trained end-to-end and hence could obtain superior performance but do not overfit the synthetic images. Experiments on clean and noisy images confirm the advantage of the proposed demosaicking method.

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来源期刊
Signal Processing-Image Communication
Signal Processing-Image Communication 工程技术-工程:电子与电气
CiteScore
8.40
自引率
2.90%
发文量
138
审稿时长
5.2 months
期刊介绍: Signal Processing: Image Communication is an international journal for the development of the theory and practice of image communication. Its primary objectives are the following: To present a forum for the advancement of theory and practice of image communication. To stimulate cross-fertilization between areas similar in nature which have traditionally been separated, for example, various aspects of visual communications and information systems. To contribute to a rapid information exchange between the industrial and academic environments. The editorial policy and the technical content of the journal are the responsibility of the Editor-in-Chief, the Area Editors and the Advisory Editors. The Journal is self-supporting from subscription income and contains a minimum amount of advertisements. Advertisements are subject to the prior approval of the Editor-in-Chief. The journal welcomes contributions from every country in the world. Signal Processing: Image Communication publishes articles relating to aspects of the design, implementation and use of image communication systems. The journal features original research work, tutorial and review articles, and accounts of practical developments. Subjects of interest include image/video coding, 3D video representations and compression, 3D graphics and animation compression, HDTV and 3DTV systems, video adaptation, video over IP, peer-to-peer video networking, interactive visual communication, multi-user video conferencing, wireless video broadcasting and communication, visual surveillance, 2D and 3D image/video quality measures, pre/post processing, video restoration and super-resolution, multi-camera video analysis, motion analysis, content-based image/video indexing and retrieval, face and gesture processing, video synthesis, 2D and 3D image/video acquisition and display technologies, architectures for image/video processing and communication.
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