In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …

Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature
Fusion based on the U-Net architecture. The proposed method is designed based on two …

Existing rain-removal algorithms often tackle either rain streak removal or raindrop removal,
and thus may fail to handle real-world rainy scenes. Besides, the lack of real-world deraining …

Adverse weather conditions in real-world scenarios lead to performance degradation of
deep learning-based detection models. A well-known method is to use image restoration …

Multi-scale feature extraction is crucial for many computer vision tasks, but it is rarely
explored in Transformer-based image super-resolution (SR) methods. In this paper, we …

Over the last decade, the number of digital images captured per day has increased
exponentially, due to the accessibility of imaging devices. The visual quality of photographs …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of point cloud denoising techniques. In this article, we first provide a …

Image denoising methods using deep neural networks have achieved a great progress in
the image restoration. However, the recovered images restored by these deep denoising …

It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in
dealing with big data collected from wind turbine operations. To address this issue, this …