Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息研究院）
Existing single-image denoising algorithms often struggle to restore details when dealing with complex noisy images. The introduction of near-infrared (NIR) images offers new possibilities for RGB image denoising. However, due to the inconsistency between NIR and RGB images, the existing works still struggle to balance the contributions of two fields in the process of image fusion. In response to this, in this paper, we develop a cross-field Frequency Correlation Exploiting Network (FCENet) for NIR-assisted image denoising. We first propose the frequency correlation prior based on an in-depth statistical frequency analysis of NIR-RGB image pairs. The prior reveals the complementary correlation of NIR and RGB images in the frequency domain. Leveraging frequency correlation prior, we then establish a frequency learning framework composed of Frequency Dynamic Selection Mechanism (FDSM) and Frequency Exhaustive Fusion Mechanism (FEFM). FDSM dynamically selects complementary information from NIR and RGB images in the frequency domain, and FEFM strengthens the control of common and differential features during the fusion process of NIR and RGB features. Extensive experiments on simulated and real data validate that the proposed method outperforms other state-of-the-art methods. The code will be released at https://github.com/yuchenwang815/FCENet.
Frequency-Decomposed INR for NIR-Assisted Low-Light RGB Image Denoising
College of Computer Science (College of Software), Inner Mongolia University, Hohhot, 010021, China（内蒙古大学计算机学院（软件学院））；School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China（武汉大学数学与统计学院）；Institute of Computational Imaging, Beijing Information Science and Technology University, Beijing 102206, China（北京信息科技大学计算成像研究院）
Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning
Beihang University（北京航空航天大学）；Image Processing Center（图像处理中心）；The State Key Laboratory of Virtual Reality Technology and Systems（虚拟现实技术与系统国家重点实验室）；Beijing Advanced Innovation Center for Biomedical Engineering（北京生物医学工程高精尖创新中心）
Noise Conscious Training of Non Local Neural Network powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising
在医疗实践中使用计算机断层扫描（CT）成像的爆炸性增长，提高了公众对患者相关辐射剂量的关注。然而，减少辐射剂量导致噪声和伪影增加，这不利地降低了扫描的可解释性。因此，一种先进的图像重建算法来提高低剂量ct的诊断性能成为研究人员的主要关注点，但由于该问题的不适定性，这具有挑战性。近年来，基于深度学习的技术已经成为低剂量CT（LDCT）去噪的主要方法。然而，仍然存在一些常见的瓶颈，这阻碍了基于深度学习的技术提供最佳性能。在这项研究中，我们试图减轻这些问题与三个新的吸积。首先，我们提出了一种新的卷积模块作为第一次尝试利用CT图像的邻域相似性去噪任务。我们提出的模块有助于提高去噪的显著幅度。接下来，我们转向CT噪声的非平稳性问题，并引入了一种新的噪声感知均方误差损失用于LDCT去噪。此外，上述损失也有助于减轻使用图像补丁训练CT去噪网络时所需的繁重工作。最后，我们提出了一种新的CT去噪任务的函数。传统的香草型协议往往忽略了精细的结构细节，而专注于全球协议。我们提出的神经网络利用自注意力和像素级GANs来恢复LDCT图像的诊断质量。我们的方法在2016年NIH-AAPM-Mayo Clinic低剂量CT Grand Challenge的公开数据集上进行了验证，其表现明显优于现有的最先进方法。
End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms
ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
提出端到端三域 TriPLET 框架，融合投影域去噪、小波域重建和图像域对抗评估，从低剂量 PET 正弦图恢复标准剂量图像，并取得最优相似度与信噪比。
Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE
imec（比利时微电子研究中心）；University of Bologna（博洛尼亚大学）；Paul Scherrer Institut（保罗·谢尔研究所）
