Self-supervised Learning to Bring Dual Reversed Rolling Shutter Images Alive

Modern consumer cameras usually employ the rolling shutter (RS) mechanism, where images are captured by scanning scenes row-by-row, yielding RS distortions for dynamic scenes. To correct RS distortions, existing methods adopt a fully supervised learning manner, where high framerate global shutter (GS) images should be collected as ground-truth supervision. In this paper, we propose a Self-supervised learning framework for Dual reversed RS distortions Correction (SelfDRSC), where a DRSC network can be learned to generate a high framerate GS video only based on dual RS images with reversed distortions. In particular, a bidirectional distortion warping module is proposed for reconstructing dual reversed RS images, and then a self-supervised loss can be deployed to train DRSC network by enhancing the cycle consistency between input and reconstructed dual reversed RS images. Besides start and end RS scanning time, GS images at arbitrary intermediate scanning time can also be supervised in SelfDRSC, thus enabling the learned DRSC network to generate a high framerate GS video. Moreover, a simple yet effective self-distillation strategy is introduced in self-supervised loss for mitigating boundary artifacts in generated GS images. On synthetic dataset, SelfDRSC achieves better or comparable quantitative metrics in comparison to state-of-the-art methods trained in the full supervision manner. On real-world RS cases, our SelfDRSC can produce high framerate GS videos with finer correction textures and better temporary consistency. The source code and trained models are made publicly available at https://github.com/shangwei5/SelfDRSC. We also provide an implementation in HUAWEI Mindspore at https://github.com/Hunter-Will/SelfDRSC-mindspore.Comment: Accepted by ICCV 2023, available at https://github.com/shangwei5/SelfDRS

Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising

Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and they have little practicality on real-world sRGB images with spatially correlated noise. Although pixel-shuffle downsampling has been suggested for breaking the noise correlation, it breaks the original information of images, which limits the denoising performance. In this paper, we propose a novel perspective to solve this problem, i.e., seeking for spatially adaptive supervision for real-world sRGB image denoising. Specifically, we take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. For flat areas, the supervision can be safely derived from non-adjacent pixels, which are much far from the current pixel for excluding the influence of the noise-correlated ones. And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas. For textured regions, the supervision has to be closely related to the content of adjacent pixels. And we present a locally aware network (LAN) to meet the requirement, while LAN itself is selectively supervised with the output of BNN. Combining these two supervisions, a denoising network (e.g., U-Net) can be well-trained. Extensive experiments show that our method performs favorably against state-of-the-art SSID methods on real-world sRGB photographs. The code is available at https://github.com/nagejacob/SpatiallyAdaptiveSSID.Comment: CVPR 2023 Camera Read

Tunable Dirac Fermion Dynamics in Topological Insulators

Three-dimensional topological insulators are characterized by insulating bulk state and metallic surface state involving Dirac fermions that behave as massless relativistic particles. These Dirac fermions are responsible for achieving a number of novel and exotic quantum phenomena in the topological insulators and for their potential applications in spintronics and quantum computations. It is thus essential to understand the electron dynamics of the Dirac fermions, i.e., how they interact with other electrons, phonons and disorders. Here we report super-high resolution angle-resolved photoemission studies on the Dirac fermion dynamics in the prototypical Bi2(Te,Se)3 topological insulators. We have directly revealed signatures of the electron-phonon coupling in these topological insulators and found that the electron-disorder interaction is the dominant factor in the scattering process. The Dirac fermion dynamics in Bi2(Te3-xSex) topological insulators can be tuned by varying the composition, x, or by controlling the charge carriers. Our findings provide crucial information in understanding the electron dynamics of the Dirac fermions in topological insulators and in engineering their surface state for fundamental studies and potential applications.Comment: 14 Pages, 4 Figure

Large-area epitaxial growth of curvature-stabilized ABC trilayer graphene.

The properties of van der Waals (vdW) materials often vary dramatically with the atomic stacking order between layers, but this order can be difficult to control. Trilayer graphene (TLG) stacks in either a semimetallic ABA or a semiconducting ABC configuration with a gate-tunable band gap, but the latter has only been produced by exfoliation. Here we present a chemical vapor deposition approach to TLG growth that yields greatly enhanced fraction and size of ABC domains. The key insight is that substrate curvature can stabilize ABC domains. Controllable ABC yields ~59% were achieved by tailoring substrate curvature levels. ABC fractions remained high after transfer to device substrates, as confirmed by transport measurements revealing the expected tunable ABC band gap. Substrate topography engineering provides a path to large-scale synthesis of epitaxial ABC-TLG and other vdW materials

Aptamer modified Zr-based porphyrinic nanoscale metal-organic frameworks for active-targeted chemo-photodynamic therapy of tumors

Active-targeted nanoplatforms could specifically target tumors compared to normal cells, making them a promising therapeutic agent. The aptamer is a kind of short DNA or RNA sequence that can specifically bind to target molecules, and could be widely used as the active targeting agents of nanoplatforms to achieve active-targeted therapy of tumors. Herein, an aptamer modified nanoplatform DOX@PCN@Apt-M was designed for active-targeted chemo-photodynamic therapy of tumors. Zr-based porphyrinic nanoscale metal organic framework PCN-224 was synthesized through a one-pot reaction, which could produce cytotoxic 1O2 for efficient treatment of tumor cells. To improve the therapeutic effect of the tumor, the anticancer drug doxorubicin (DOX) was loaded into PCN-224 to form DOX@PCN-224 for tumor combination therapy. Active-targeted combination therapy achieved by modifying the MUC1 aptamer (Apt-M) onto DOX@PCN-224 surface can not only further reduce the dosage of therapeutic agents, but also reduce their toxic and side effects on normal tissues. In vitro, experimental results indicated that DOX@PCN@Apt-M exhibited enhanced combined therapeutic effect and active targeting efficiency under 808 nm laser irradiation for MCF-7 tumor cells. Based on PCN-224 nanocarriers and aptamer MUC1, this work provides a novel strategy for precisely targeting MCF-7 tumor cells

Gapless surface Dirac cone in antiferromagnetic topological insulator MnBi2_2Te4_4

The recent discovered antiferromagnetic topological insulators in Mn-Bi-Te family with intrinsic magnetic ordering have rapidly drawn broad interest since its cleaved surface state is believed to be gapped, hosting the unprecedented axion states with half-integer quantum Hall effect. Here, however, we show unambiguously by using high-resolution angle-resolved photoemission spectroscopy that a gapless Dirac cone at the (0001) surface of MnBi$_2$Te$_4$ exists between the bulk band gap. Such unexpected surface state remains unchanged across the bulk N\'eel temperature, and is even robust against severe surface degradation, indicating additional topological protection. Through symmetry analysis and $\textit{ab}$-$\textit{initio}$ calculations we consider different types of surface reconstruction of the magnetic moments as possible origins giving rise to such linear dispersion. Our results reveal that the intrinsic magnetic topological insulator hosts a rich platform to realize various topological phases such as topological crystalline insulator and time-reversal-preserved topological insulator, by tuning the magnetic configurations.Comment: 9 pages, 4 figures. To appear in Phys. Rev. X. See Version 1 for the supplementary fil