online read us now
Paper details
Number 4 - December 2023
Volume 33 - 2023
Denseformer for single image deraining
Tianming Wang, Kaige Wang, Qing Li
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.
Keywords
artificial intelligence, convolutional neural network, image deraining