MLTDNet: an efficient multi-level transformer network for single image deraining

Gao, Feng; Mu, Xiangyu; Ouyang, Chao; Yang, Kai; Ji, Shengchang; Guo, Jie; Wei, Haokun; Wang, Nan; Ма, Лей; Yang, Biao

doi:10.1007/s00521-022-07226-0

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…proposed an SDNet to apply the Swin Transformer in the field of single‐image de‐rain by improving the basic module of the Swin Transformer and designing a three‐branch model to realise single‐image de‐raining. Gao [38] et al. proposed a Multi‐level Transformer Network (MLTDNet), which is a multilevel Transformer network for the single image de‐rain problem.…”

Section: Related Workmentioning

confidence: 99%

Recursive attention collaboration network for single image de‐raining

Li,

Gong

et al. 2024

IET Cyber-Syst and Robotics

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Single‐image rain removal is an important problem in the field of computer vision aimed at recovering clean images from rainy images. In recent years, data‐driven convolutional neural network (CNN)‐based rain removal methods have achieved significant results, but most of them cannot fully focus on the contextual information in rain‐containing images, which leads to the failure of recovering some of the background details of the images that have been corrupted due to the aggregation of rain streaks. With the success of Transformer‐based models in the field of computer vision, global features can be easily acquired to better help recover details in the background of an image. However, Transformer‐based models often require a large number of parameters during the training process, which makes the training process very difficult and makes it difficult to apply them to specific devices for execution in reality. The authors propose a Recursive Attention Collaboration Network, which consists of a recursive Swin‐transformer block (STB) and a CNN‐based feature fusion block. The authors designed the Recursively Integrate Transformer Block (RITB), which consists of several STBs recursively connected, that can effectively reduce the number of parameters of the model. The final part of the module can integrate the local information from the STBs. The authors also design the Feature Enhancement Block, which can better recover the details of the background information corrupted by rain streaks of different density shapes through the features passed from the RITB. Experiments show that the proposed network has an effective rain removal effect on both synthetic and real datasets and has fewer model parameters than other mainstream methods.

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Section: Related Workmentioning

confidence: 99%

Recursive attention collaboration network for single image de‐raining

Li,

Gong

et al. 2024

IET Cyber-Syst and Robotics

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“…Convolutional neural network : Due to the outstanding performance of convolution neural networks (CNNs) in image content analysis (Krizhevsky et al, 2017), they have been widely used to extract high‐level features (Bhatt et al, 2021; Gao et al, 2022; Lu et al, 2021; Sun, Zhang, Jiang, et al, 2021; Xie et al, 2021; Ye et al, 2015). To reduce the irrelevance of ads, Xiang et al (2015) used a CNN to extract both the ad features and the image features to match and select the most salient of the relevant ads.…”

Section: Computer Science Studiesmentioning

confidence: 99%

A survey of online video advertising

Zhang

Yan

et al. 2023

WIREs Data Min & Knowl

Self Cite

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With the development of social media and the ubiquity of the Internet, recent years have witnessed the rapid development of online video advertising among publishers and advertisers. Video advertising, as a new type of advertisement, has gained significant research attention from both academia and industry, coinciding with the ever-growing volume of online videos. In this research, we provide a comprehensive survey of online video advertising in the fields of social science and computer science. We investigate state-of-the-art articles from 1990 to the present and provide a new taxonomy of extant research topics based on these articles. We also highlight the factors that cause advertising to affect people and the most popular video advertising techniques used in computer science. Finally, on the basis of the analytics of the surveyed papers, future challenges are identified and potential solutions to these are discussed.

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