Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges

By Wenqi Ren, Jin-shan Pan, Hua Zhang, Xiaochun Cao, Ming-Hsuan Yang. International Journal of Computer Vision, 2019

361 citations recorded by the source corpus.

How researchers see this paper

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CLEARER: Multi-Scale Neural Architecture Search for Image Restoration (2020, background): “In these handcrafted models, multi-scale architectures [16, 37, 33, 35, 39, 42] have played a significant role in improving the performance, of which the basic idea is to fuse features with different resolutions/scales.”
DEA-Net: Single Image Dehazing Based on Detail-Enhanced Convolution and Content-Guided Attention (2023, methodology): “For instance, DehazeNet [2], MSCNN [7] and MSCNN-HE [23] employ CNNs for transmission map estimation.”
Image De-Raining Transformer (2022, background): “With the advent of the deep learning era [12], convolutional neural networks (CNNs) [13], [14], [15] have proven extremely successful for various low-level vision tasks [16], [17], [18], [19], [20].”
Image De-raining via Continual Learning (2021, background): “In recent years, remarkable progress has been achieved on single image de-raining and other low-level vision tasks due to the rapid growth of deep learning [32, 9, 5, 35, 45, 18, 50, 43, 27, 8, 44, 41, 33, 23, 13, 37, 3, 6, 30].”
Deep Learning on Image Denoising: An overview (2019, positive): “Based on the reasons above, convolutional neural networks (CNNs) were proposed [141, 180].”