Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing
Published in
arXiv.org(2018)External Links:
TL
TL;DR
This paper proposes an end-to-end generative method for image dehazing based on designing a fully convolutional neural network to recognize haze structures in input images and restore clear, haze-free images.
Abstract
Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis. This paper proposes an end-to-end generative method for image dehazing. It is based on designing a fully convolutional neural network to recognize haze structures in input images and restore clear, haze-free images. The proposed method is agnostic in the sense that it does not explore the atmosphere scattering model. Somewhat surprisingly, it achieves superior performance relative to all existing state-of-the-art methods for image dehazing even on SOTS outdoor images, which are synthesized using the atmosphere scattering model. Project detail and code can be found here: this https URL
References20 items
1
Gated Fusion Network for Single Image Dehazing
2
RESIDE: A Benchmark for Single Image Dehazing
3
An All-in-One Network for Dehazing and Beyond
4
Single Image Dehazing via Multi-scale Convolutional Neural Networks
5