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CascadedGaze: Efficiency in Global Context Extraction for Image Restoration

TMLR 2024(2024)

Researcher | Principal Researcher | Full Professor

Cited 0|Views12
Abstract
Image restoration tasks traditionally rely on convolutional neural networks. However, given the local nature of the convolutional operator, they struggle to capture global information. The promise of attention mechanisms in Transformers is to circumvent this problem, but it comes at the cost of intensive computational overhead. Many recent studies in image restoration have focused on solving the challenge of balancing performance and computational cost via Transformer variants. In this paper, we present CascadedGaze Network (CGNet), an encoder-decoder architecture that employs Global Context Extractor (GCE), a novel and efficient way to capture global information for image restoration. The GCE module leverages small kernels across convolutional layers to learn global dependencies, without requiring self-attention. Extensive experimental results show that our approach outperforms a range of state-of-the-art methods on denoising benchmark datasets including both real image denoising and synthetic image denoising, as well as on image deblurring task, while being more computationally efficient.
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Image Retrieval,Image Segmentation,Image Annotation,Object Recognition
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要点:本文提出了一种名为CascadedGaze网络的图像恢复方法,通过引入全局上下文提取器(Global Context Extractor, GCE)来高效地捕捉全局信息,并在图像去噪和图像去模糊任务上超越了许多最先进的方法。

方法:本文采用了一种编码器-解码器架构,其中GCE模块利用了卷积层上的小核以学习全局依赖关系,而无需使用自注意力机制。

实验:通过广泛的实验结果表明,我们的方法在包括真实图像去噪、合成图像去噪以及图像去模糊任务上,相比许多最先进的方法更具有计算效率,并取得了更好的效果。