Multi-modal fusion combines multiple modal information to overcome the limitation of
incomplete information expressed by a single modality, so as to realize the complementarity …

Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image
processing and computer vision, which can integrate images with multiple exposure levels …

Convolutional neural networks, with a powerful ability for feature representation, have made
vast inroads into motor fault diagnosis. However, most of the existing CNN models cannot …

CNN-based fault diagnosis approaches have achieved promising results in improving the
safety and reliability of rotating machinery. Most of the existing CNN models are developed …

Image captured under poor-illumination conditions often display attributes of having poor
contrasts, low brightness, a narrow gray range, colour distortions and considerable …

Image enhancement is an essential technique used in many imaging applications. The main
motivation of image enhancement is processing an image to be more suitable for specific …

Abstract The Retinex Theory (RT) and its adaptations have gained significant popularity in
the field of image processing. Nevertheless, traditional Retinex algorithms are generally …

In real scenes, due to the problems of low light and unsuitable views, the images often
exhibit a variety of degradations, such as low contrast, color distortion, and noise. These …

Multimodal Image fusion is becoming urgent in multi-sensor information utilization. However,
existing end-to-end image fusion frameworks ignore a priori knowledge integration and long …

Recent advances in convolutional neural networks (CNN) have boosted the research on
reliability monitoring of rotating machinery. In actual industry production, the mechanical …