Haze Removal Using Aggregated Resolution Convolution Network

8Citations
Citations of this article
15Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

The haze removal technique refers to the process of reconstructing haze-free images from scenes of inclement weather conditions. This task has an extensive demand in practical applications. At present, models based on deep convolution neural networks have made significant progress in the haze removal field, greatly outperforming the traditional prior and constraint methods. However, the current CNNs methods, which involve only a single input image, do not provide sufficient features to determine the optimal transmission maps for haze removal; therefore, we propose and design an aggregated resolution convolution network (ARCN) that uses multiple inputs and aggregates features from a CNN model and the adversarial loss algorithm. Experiments comparing the visual results of our network with those of several previous methods reveal substantial improvements.

Cite

CITATION STYLE

APA

He, L., Bai, J., & Ru, L. (2019). Haze Removal Using Aggregated Resolution Convolution Network. IEEE Access, 7, 123698–123709. https://doi.org/10.1109/ACCESS.2019.2938218

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free