Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and photon-counting computed tomography (CT) denoising can optimize diagnostic performance at minimized radiation dose. ...Supervised deep denoising methods are popular but require paired clean or noisy samples that are often unavailable in practice. Limited by the independent noise assumption, current self-supervised denoising methods cannot process correlated noises as in CT images. Here we propose the first-of-its-kind similarity-based self-supervised deep denoising approach, referred to as Noise2Sim, that works in a nonlocal and nonlinear fashion to suppress not only independent but also correlated noises. Theoretically, Noise2Sim is asymptotically equivalent to supervised learning methods under mild conditions. Experimentally, Nosie2Sim recovers intrinsic features from noisy low-dose CT and photon-counting CT images as effectively as or even better than supervised learning methods on practical datasets visually, quantitatively and statistically. Noise2Sim is a general self-supervised denoising approach and has great potential in diverse applications.
In this paper, we propose a novel weakly supervised semantic segmentation (WSSS) method that uses image tags as supervision to achieve joint pixel-level localization of the key local structure (KLS) ...and image-level classification of the aurora images captured by the ground-based optical all-sky imager. First, a patch-scale model (PSM) based on the small-scale structure of aurora is designed to identify the type-specific regions for each training image. Second, a region-scale model is trained with the identified type-specific regions to coarsely localize the KLS from multiple sizes of field of view, based on which the aurora image is classified. Finally, given the predicted image type, the PSM further refines the KLS in a pixel level. By localizing KLS from coarse to fine, the proposed method captures both overall shape with a bottom-up processing and local structure details of aurora in a top-down manner. Extensive experiments on the expert labeled data sets have demonstrated the efficacy of the proposed method in benchmarking with the state-of-the-art WSSS methods.
An efficient palladium-catalyzed reaction of 60fullerene with benzoic acids via carboxylic acid group-directed C-H bond activation is achieved. The obtained 60fullerene-fused lactones can undergo a ...retro Baeyer-Villiger reaction to provide 60fullerene-fused ketones via apparent reduction in the presence of triflic acid. A representative ketone product obtained by the reduction reaction can be employed as an overcoating layer for the electron-transporting layer in an n-type perovskite solar cell.An efficient palladium-catalyzed reaction of 60fullerene with benzoic acids via carboxylic acid group-directed C-H bond activation is achieved. The obtained 60fullerene-fused lactones can undergo a retro Baeyer-Villiger reaction to provide 60fullerene-fused ketones via apparent reduction in the presence of triflic acid. A representative ketone product obtained by the reduction reaction can be employed as an overcoating layer for the electron-transporting layer in an n-type perovskite solar cell.
An efficient palladium-catalyzed reaction of 60fullerene with benzoic acids via carboxylic acid group-directed C–H bond activation is achieved. The obtained 60fullerene-fused lactones can undergo a ...retro Baeyer–Villiger reaction to provide 60fullerene-fused ketones via apparent reduction in the presence of triflic acid. A representative ketone product obtained by the reduction reaction can be employed as an overcoating layer for the electron-transporting layer in an n-type perovskite solar cell.
Metal implants and other high-density objects in patients introduce severe streaking artifacts in CT images, compromising image quality and diagnostic performance. Although various methods were ...developed for CT metal artifact reduction over the past decades, including the latest dual-domain deep networks, remaining metal artifacts are still clinically challenging in many cases. Here we extend the state-of-the-art dual-domain deep network approach into a quad-domain counterpart so that all the features in the sinogram, image, and their corresponding Fourier domains are synergized to eliminate metal artifacts optimally without compromising structural subtleties. Our proposed quad-domain network for MAR, referred to as Quad-Net, takes little additional computational cost since the Fourier transform is highly efficient, and works across the four receptive fields to learn both global and local features as well as their relations. Specifically, we first design a Sinogram-Fourier Restoration Network (SFR-Net) in the sinogram domain and its Fourier space to faithfully inpaint metal-corrupted traces. Then, we couple SFR-Net with an Image-Fourier Refinement Network (IFR-Net) which takes both an image and its Fourier spectrum to improve a CT image reconstructed from the SFR-Net output using cross-domain contextual information. Quad-Net is trained on clinical datasets to minimize a composite loss function. Quad-Net does not require precise metal masks, which is of great importance in clinical practice. Our experimental results demonstrate the superiority of Quad-Net over the state-of-the-art MAR methods quantitatively, visually, and statistically. The Quad-Net code is publicly available at https://github.com/longzilicart/Quad-Net .
The electrosynthesis of decorated basket molecules, that is, 60fullerene-fused 12-membered macrolactones, has been achieved efficiently for the first time by the electrochemical reduction of ...60fullerene-fused 6-membered lactones and subsequent ring expansion with 1,2-bis(1-bromoalkyl)benzenes. The observed isomeric distributions of the obtained macrolactones are elucidated by theoretical calculations. The product structures have been firmly established by single-crystal X-ray analyses.
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