This study reviews the technique of convolutional neural network (CNN) applied in a specific
field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on …

Blurriness is annoying yet common in digital images. Many sharpness assessment
indicators using handcrafted features achieve impressive results on synthesized blurring …

Air quality monitoring is important to the green development of smart cities. Several technical
challenges exist for intelligent, high-precision monitoring, such as computing overhead, area …

We propose a new incremental aggregation algorithm for multi-image deblurring with
automatic image selection. The primary motivation is that current burst deblurring methods …

Deep neural network is a hotspot in the field of Machine Learning, which can realize deep
hierarchical representation of input data. In this paper, a simplified Shallow Convolutional …

Accurate realistic blurred image quality assessment (RB-IQA) is challenging due to potential
occurrence of blurring in image acquistion, compression and transmission. Derived from the …

The traditional technology of radar echo image extrapolation for rainfall nowcasting faces
such problems as insufficiently high accuracy, the incomplete analysis of the data on radar …

An image quality evaluation method of brightness contrast is proposed. It is assumed that
the change trend of gradual enhancement of an image with a high contrast is different with …

1 Résumé Notre objectif est la détection de flou en temps réel sans référence et sans
apprentissage dans des images peu texturées. Cette phase est utile, par exemple, pour la …

Our previous study proposed a shallow convolutional neural network (CNN) to quantify the
sharpness of natural images. The network utilized a multilayer perceptron (MLP) as its …