Deep Plug-and-Play Prior for Hyperspectral Image Restoration

Deep-learning-based hyperspectral image (HSI) restoration methods have gained great popularity for their remarkable performance but often demand expensive network retraining whenever the specifics of task changes. In this paper, we propose to restore HSIs in a unified approach with an effective plug-and-play method, which can jointly retain the flexibility of optimization-based methods and utilize the powerful representation capability of deep neural networks. Specifically, we first develop a new deep HSI denoiser leveraging gated recurrent convolution units, short- and long-term skip connections, and an augmented noise level map to better exploit the abundant spatio-spectral information within HSIs. It, therefore, leads to the state-of-the-art performance on HSI denoising under both Gaussian and complex noise settings. Then, the proposed denoiser is inserted into the plug-and-play framework as a powerful implicit HSI prior to tackle various HSI restoration tasks. Through extensive experiments on HSI super-resolution, compressed sensing, and inpainting, we demonstrate that our approach often achieves superior performance, which is competitive with or even better than the state-of-the-art on each task, via a single model without any task-specific training.Comment: code at https://github.com/Zeqiang-Lai/DPHSI

Instance Segmentation in the Dark

Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments. In this work, we take a deep look at instance segmentation in the dark and introduce several techniques that substantially boost the low-light inference accuracy. The proposed method is motivated by the observation that noise in low-light images introduces high-frequency disturbances to the feature maps of neural networks, thereby significantly degrading performance. To suppress this ``feature noise", we propose a novel learning method that relies on an adaptive weighted downsampling layer, a smooth-oriented convolutional block, and disturbance suppression learning. These components effectively reduce feature noise during downsampling and convolution operations, enabling the model to learn disturbance-invariant features. Furthermore, we discover that high-bit-depth RAW images can better preserve richer scene information in low-light conditions compared to typical camera sRGB outputs, thus supporting the use of RAW-input algorithms. Our analysis indicates that high bit-depth can be critical for low-light instance segmentation. To mitigate the scarcity of annotated RAW datasets, we leverage a low-light RAW synthetic pipeline to generate realistic low-light data. In addition, to facilitate further research in this direction, we capture a real-world low-light instance segmentation dataset comprising over two thousand paired low/normal-light images with instance-level pixel-wise annotations. Remarkably, without any image preprocessing, we achieve satisfactory performance on instance segmentation in very low light (4~\% AP higher than state-of-the-art competitors), meanwhile opening new opportunities for future research.Comment: Accepted by International Journal of Computer Vision (IJCV) 202

5 x 20 Gb/s III-V on silicon electroabsorption modulator array heterogeneously integrated with a 1.6nm channel-spacing silicon AWG

We demonstrate a five-channel wavelength division multiplexed modulator module that heterogeneously integrates a 1.6nm channel-spacing arrayed-waveguide grating and a 20Gbps electroabsorption modulator array, showing the potential for 100 Gbps capacity on a 1.5x0.5 mm(2) footprint

Low-mass dark matter search results from full exposure of PandaX-I experiment

We report the results of a weakly-interacting massive particle (WIMP) dark matter search using the full 80.1\;live-day exposure of the first stage of the PandaX experiment (PandaX-I) located in the China Jin-Ping Underground Laboratory. The PandaX-I detector has been optimized for detecting low-mass WIMPs, achieving a photon detection efficiency of 9.6\%. With a fiducial liquid xenon target mass of 54.0\,kg, no significant excess event were found above the expected background. A profile likelihood analysis confirms our earlier finding that the PandaX-I data disfavor all positive low-mass WIMP signals reported in the literature under standard assumptions. A stringent bound on the low mass WIMP is set at WIMP mass below 10\,GeV/c$^2$, demonstrating that liquid xenon detectors can be competitive for low-mass WIMP searches.Comment: v3 as accepted by PRD. Minor update in the text in response to referee comments. Separating Fig. 11(a) and (b) into Fig. 11 and Fig. 12. Legend tweak in Fig. 9(b) and 9(c) as suggested by referee, as well as a missing legend for CRESST-II legend in Fig. 12 (now Fig. 13). Same version as submitted to PR

Re-recognize early recurrence of persistent atrial fibrillation

AimsFew studies on early recurrence (ER) focused on patients with persistent atrial fibrillation (AF). We aimed to investigate the characteristics and clinical significance of ER in patients with persistent AF after catheter ablation (CA).MethodsA total of 348 consecutive patients who underwent first-time CA for persistent and long-standing persistent AF between January 2019 and May 2022 were investigated.ResultsAbout 5/348 (1.44%) patients who failed to convert to sinus rhythm after CA were excluded. A total of 110/343 (32.1%) patients had ER, in which 98 (89.1%) were persistent and 50.9% occurred in the first 24 h after CA. Compared with the patients without ER, those with ER were more likely to have late recurrence (LR) (92.7% vs. 1.7%, P < 0.001) during a median follow-up of 13 (IQR 6–23) months. ER was the most significant independent predictor for LR (OR 120.5, 95% CI 41.5–349.8, P < 0.001). ER as atrial flutter (AFL) had a lower risk of LR when compared with ER as AF (P = 0.011) and both AF and AFL (P = 0.003). Early intervention of the patient with ER improved the short-term outcomes (P < 0.001), not long-term outcomes. Only 22/251 (8.76%) patients of LR appears among those who had no recurrence in the first month.ConclusionsPatients with persistent AF may not have a blanking period but rather have a risk period. Clinical significance of the blanking period should be given differential treatment between paroxysmal AF and persistent AF