<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Image is one of the most important forms of information expression in multimedia. It is the key factor to determine the visual effect of multimedia software. As an image restoration task, image deraining can effectively restore the original information of the image, which is conducive to the downstream task. In recent years, with the development of deep learning technology, CNN and Transformer structures have shone brightly in computer vision. In this paper, we summarize the key to success of these structures in the past, and on this basis, we introduce the concept of a layer aggregation mechanism to describe how to reuse the information of the previous layer to better extract the features of the current layer. Based on this layer aggregation mechanism, we build the rain removal network called DenseformerNet. Our network strengthens feature promotion and encourages feature reuse, allowing better information and gradient flow. Through a large number of experiments, we prove that our model is efficient and effective, and expect to bring some illumination to the future rain removal network.</p>
</abstract>

Image is one of the most important forms of information expression in multimedia. It is the key factor to determine the visual effect of multimedia software. As an image restoration task, image deraining can effectively restore the original information of the image, which is conducive to the downstream task. In recent years, with the development of deep learning technology, CNN and Transformer structures have shone brightly in computer vision. In this paper, we summarize the key to success of these structures in the past, and on this basis, we introduce the concept of a layer aggregation mechanism to describe how to reuse the information of the previous layer to better extract the features of the current layer. Based on this layer aggregation mechanism, we build the rain removal network called DenseformerNet. Our network strengthens feature promotion and encourages feature reuse, allowing better information and gradient flow. Through a large number of experiments, we prove that our model is efficient and effective, and expect to bring some illumination to the future rain removal network.

Denseformer for Single Image Deraining

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Denseformer for Single Image Deraining"}

Image is one of the most important forms of information expression in multimedia. It is the key factor to determine the visual effect of multimedia software....

Denseformer for Single Image Deraining

Online veröffentlicht: 21. Dez. 2023

Seitenbereich: 651 - 661

Eingereicht: 04. Dez. 2022

Akzeptiert: 19. Apr. 2023

DOI: https://doi.org/10.34768/amcs-2023-0046

Schlüsselwörter
artificial intelligence, convolutional neural network, image deraining

© 2023 Tianming Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Denseformer for Single Image Deraining

Online veröffentlicht: 21. Dez. 2023

Seitenbereich: 651 - 661

Eingereicht: 04. Dez. 2022

Akzeptiert: 19. Apr. 2023

DOI: https://doi.org/10.34768/amcs-2023-0046

Schlüsselwörterartificial intelligence, convolutional neural network, image deraining

© 2023 Tianming Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
artificial intelligence, convolutional neural network, image deraining