Browsing by Author "Shu, Minglei"

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    Not All Areas Are Equal: A Novel Separation-Restoration-Fusion Network for Image Raindrop Removal
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Ren, Dongdong; Li, Jinbao; Han, Meng; Shu, Minglei; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Detecting and removing raindrops from an image while keeping the high quality of image details has attracted tremendous studies, but remains a challenging task due to the inhomogeneity of the degraded region and the complexity of the degraded intensity. In this paper, we get rid of the dependence of deep learning on image-to-image translation and propose a separationrestoration- fusion network for raindrops removal. Our key idea is to recover regions of different damage levels individually, so that each region achieves the optimal recovery result, and finally fuse the recovered areas. In the region restoration module, to complete the restoration of a specific area, we propose a multi-scale feature fusion global information aggregation attention network to achieve global to local information aggregation. Besides, we also design an inside and outside dense connection dilated network, to ensure the fusion of the separated regions and the fine restoration of the image. The qualitatively and quantitatively evaluations are conducted to evaluate our method with the latest existing methods. The result demonstrates that our method outperforms state-of-the-art methods by a large margin on the benchmark datasets in extensive experiments.
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    SCGA-Net: Skip Connections Global Attention Network for Image Restoration
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Ren, Dongdong; Li, Jinbao; Han, Meng; Shu, Minglei; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Deep convolutional neural networks (DCNN) have shown their advantages in the image restoration tasks. But most existing DCNN-based methods still suffer from the residual corruptions and coarse textures. In this paper, we propose a general framework ''Skip Connections Global Attention Network'' to focus on the semantics delivery from shallow layers to deep layers for low-level vision tasks including image dehazing, image denoising, and low-light image enhancement. First of all, by applying dense dilated convolution and multi-scale feature fusion mechanism, we establish a novel encoder-decoder network framework to aggregate large-scale spatial context and enhance feature reuse. Secondly, the solution we proposed for skipping connection uses attention mechanism to constraint information, thereby enhancing the high-frequency details of feature maps and suppressing the output of corruptions. Finally, we also present a novel attention module dubbed global constraint attention, which could effectively captures the relationship between pixels on the entire feature maps, to obtain the subtle differences among pixels and produce an overall optimal 3D attention maps. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods in image dehazing, image denoising, and low-light image enhancement.