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Computer Science, Information Systems
Xiao Tan et al.
Summary: This paper proposes a deep neural network called Deep SR-HDR for the joint task of super-resolution and high dynamic range imaging. The network reconstructs high-resolution HDR images from a set of differently exposed low-resolution LDR images of a dynamic scene. By merging the shared processing steps and designing a multi-scale deformable module, the proposed network achieves high-quality image reconstruction efficiently.
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Article
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Summary: This study introduces a semi-supervised semantic segmentation method that reduces the need for a large number of pixel-level annotated images by improving the confidence of the predicted class probability map. The method includes adversarial learning and information entropy calculation, resulting in more confident predictions focused on misclassified regions, particularly boundary areas, and achieving competitive segmentation performance.
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Summary: In this work, we propose a novel lightweight anomaly detection model for weakly-supervised video anomaly detection. The model fully utilizes normal videos to train a classifier with discriminative ability for normal videos, and employs a contrastive attention module to improve the selection of anomalous segments. Experimental results demonstrate that our model significantly improves the frame-level AUC compared to state-of-the-art methods.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
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Summary: This paper proposes a multiframe-to-multiframe (MM) denoising scheme that simultaneously recovers multiple clean frames from consecutive noisy frames, aiming to achieve better temporal consistency. Furthermore, an MM network (MMNet) is presented, which combines interframe similarity and single-frame characteristics to achieve competitive denoising performance.
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(2022)
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Summary: The study introduces a motion estimation and compensation driven neural network for video frame interpolation, which integrates optical flow and interpolation kernels using an adaptive warping layer. It achieves visually appealing results without the need for hand-crafted features, showing improved computational efficiency compared to existing methods. The proposed MEMC-Net architecture can be seamlessly adapted to various video enhancement tasks and outperforms state-of-the-art algorithms on a wide range of datasets in quantitative and qualitative evaluations.
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(2021)
Article
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Summary: The study proposes a demand-oriented framework for image denoising, which can balance denoising quality, number of parameters, and computational complexity. By designing a scale encoder, split-flow module, and scale decoder, the framework achieves competitive denoising performance in terms of parameters and complexity, as demonstrated by extensive experiments.
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Article
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Summary: This paper presents a novel deep learning-based video denoising method, MAP-VDNet, which efficiently removes noise by exploiting temporal redundancy in video frames using the MAP estimation framework. The algorithm allows for effective separation of motion estimation and image restoration, and outperforms current state-of-the-art techniques on popular video datasets.
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(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
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Summary: The paper introduces a new method for utilizing self-similarity in video denoising, while still relying on a regular convolutional architecture. By introducing the concept of "patch-craft frames" to enhance video sequences with artificial frames, significant improvements in denoising performance were achieved.
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(2021)
Article
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Axel Davy et al.
Summary: Recent research has shown that non-local patch-based methods were once state of the art for image denoising, but are now surpassed by CNNs. However, in video denoising, non-local methods are still competitive due to their ability to exploit video temporal redundancy. The proposed method in this study incorporates non-locality into a CNN to improve image and video denoising results.
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(2018)
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Alexey Dosovitskiy et al.
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Matteo Maggioni et al.
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Shuiwang Ji et al.
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(2013)
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Asmaa Hosni et al.
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(2013)
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Matteo Maggioni et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
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