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Jing Ye

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5

ICRA Conference 2024 Conference Paper

ASP-LED: Learning Ambiguity-Aware Structural Priors for Joint Low-Light Enhancement and Deblurring

  • Jing Ye
  • Yang Liu 0434
  • Congjing Yu
  • Changzhen Qiu
  • Zhiyong Zhang 0005

Low-light enhancement and deblurring is vital for high-level vision-related nighttime tasks. Most existing cascade and joint enhancement methods may provide undesirable results, suffering from severe artifacts, deteriorating blur, and unclear details. In this paper, we propose a novel ambiguity-aware network (ASP-LED) with structural priors, including high-frequency and edge, to enable effective image representation learning for joint low-light enhancement and deblurring. Specifically, we employ a Transformer backbone to explore the global clues of the image. To compensate for the inadequate local detail optimization, we propose a multi-patch perception pyramid block that models the correlation between different size patches and ambiguity, and identifies non-uniform deblurring spatial features, facilitating the reconstruction of potential high-frequency and edge information. Furthermore, a prior-guided reconstruction block based on the parallel attention mechanism is present to adaptively correct global image with statistical features, which helps guide the model to refine sharp texture and structure. Extensive experiments performed on simulated and real-world datasets demonstrate the efficacy of our proposed method in restoring low-light blurry images with increased visual perception compared to state-of-the-art methods.

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IJCAI Conference 2019 Conference Paper

BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series

  • Bin Zhou
  • Shenghua Liu
  • Bryan Hooi
  • Xueqi Cheng
  • Jing Ye

Given a large-scale rhythmic time series containing mostly normal data segments (or `beats'), can we learn how to detect anomalous beats in an effective yet efficient way? For example, how can we detect anomalous beats from electrocardiogram (ECG) readings? Existing approaches either require excessively high amounts of labeled and balanced data for classification, or rely on less regularized reconstructions, resulting in lower accuracy in anomaly detection. Therefore, we propose BeatGAN, an unsupervised anomaly detection algorithm for time series data. BeatGAN outputs explainable results to pinpoint the anomalous time ticks of an input beat, by comparing them to adversarially generated beats. Its robustness is guaranteed by its regularization of reconstruction error using an adversarial generation approach, as well as data augmentation using time series warping. Experiments show that BeatGAN accurately and efficiently detects anomalous beats in ECG time series, and routes doctors' attention to anomalous time ticks, achieving accuracy of nearly 0. 95 AUC, and very fast inference (2. 6 ms per beat). In addition, we show that BeatGAN accurately detects unusual motions from multivariate motion-capture time series data, illustrating its generality.

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