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Ri Cheng

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4 papers

1 author row

AAAI Conference 2025 Conference Paper

Efficient Online Training for Zero-Shot Time-Lapse Microscopy Denoising and Super-Resolution

Ruian He
Ri Cheng
Xinkai Lyu
Weimin Tan
Bo Yan

In time-lapse microscopy, inherent noise significantly limits imaging sensitivity and increases measurement uncertainty. Due to the scarcity of clean data, zero-shot approaches have emerged as highly data-efficient solutions for microscopy denoising. However, existing methods typically process video frames independently, resulting in long training times and issues such as temporal noise and over-smoothing. In this paper, we introduce MDSR-Zero, a zero-shot online learning method designed for plug-and-play noise suppression and super-resolution of microscopy videos. Our approach leverages an efficient online training strategy that reuses denoising models from previous frames. By treating the video as a continuous stream, our model significantly reduces training time and ensures temporally consistent denoising. Additionally, we propose a novel loss function tailored for denoising in the context of super-resolution, which enhances the detail in the denoised results. Extensive experiments on both synthetic and real-world noise demonstrate that our method achieves state-of-the-art performance among zero-shot denoising approaches and is competitive with self-supervised methods. Notably, our method can reduce training time by up to 10x compared to the previous SOTA method.

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AAAI Conference 2024 Conference Paper

Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation

Ri Cheng
Ruian He
Xuhao Jiang
Shili Zhou
Weimin Tan
Bo Yan

Existing recurrent optical flow estimation networks are computationally expensive since they use a fixed large number of iterations to update the flow field for each sample. An efficient network should skip iterations when the flow improvement is limited. In this paper, we develop a Context-Aware Iteration Policy Network for efficient optical flow estimation, which determines the optimal number of iterations per sample. The policy network achieves this by learning contextual information to realize whether flow improvement is bottlenecked or minimal. On the one hand, we use iteration embedding and historical hidden cell, which include previous iterations information, to convey how flow has changed from previous iterations. On the other hand, we use the incremental loss to make the policy network implicitly perceive the magnitude of optical flow improvement in the subsequent iteration. Furthermore, the computational complexity in our dynamic network is controllable, allowing us to satisfy various resource preferences with a single trained model. Our policy network can be easily integrated into state-of-the-art optical flow networks. Extensive experiments show that our method maintains performance while reducing FLOPs by about 40%/20% for the Sintel/KITTI datasets.

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AAAI Conference 2024 Conference Paper

Low-Latency Space-Time Supersampling for Real-Time Rendering

Ruian He
Shili Zhou
Yuqi Sun
Ri Cheng
Weimin Tan
Bo Yan

With the rise of real-time rendering and the evolution of display devices, there is a growing demand for post-processing methods that offer high-resolution content in a high frame rate. Existing techniques often suffer from quality and latency issues due to the disjointed treatment of frame supersampling and extrapolation. In this paper, we recognize the shared context and mechanisms between frame supersampling and extrapolation, and present a novel framework, Space-time Supersampling (STSS). By integrating them into a unified framework, STSS can improve the overall quality with lower latency. To implement an efficient architecture, we treat the aliasing and warping holes unified as reshading regions and put forth two key components to compensate the regions, namely Random Reshading Masking (RRM) and Efficient Reshading Module (ERM). Extensive experiments demonstrate that our approach achieves superior visual fidelity compared to state-of-the-art (SOTA) methods. Notably, the performance is achieved within only 4ms, saving up to 75\% of time against the conventional two-stage pipeline that necessitates 17ms.

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AAAI Conference 2024 Conference Paper

MGQFormer: Mask-Guided Query-Based Transformer for Image Manipulation Localization

Kunlun Zeng
Ri Cheng
Weimin Tan
Bo Yan

Deep learning-based models have made great progress in image tampering localization, which aims to distinguish between manipulated and authentic regions. However, these models suffer from inefficient training. This is because they use ground-truth mask labels mainly through the cross-entropy loss, which prioritizes per-pixel precision but disregards the spatial location and shape details of manipulated regions. To address this problem, we propose a Mask-Guided Query-based Transformer Framework (MGQFormer), which uses ground-truth masks to guide the learnable query token (LQT) in identifying the forged regions. Specifically, we extract feature embeddings of ground-truth masks as the guiding query token (GQT) and feed GQT and LQT into MGQFormer to estimate fake regions, respectively. Then we make MGQFormer learn the position and shape information in ground-truth mask labels by proposing a mask-guided loss to reduce the feature distance between GQT and LQT. We also observe that such mask-guided training strategy has a significant impact on the convergence speed of MGQFormer training. Extensive experiments on multiple benchmarks show that our method significantly improves over state-of-the-art methods.

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