A Novel Residual Dense Pyramid Network for Image Dehazing
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{yin:2019:Entropy,
-
author = "Shibai Yin and Yibin Wang and Yee-Hong Yang",
-
title = "A Novel Residual Dense Pyramid Network for Image
Dehazing",
-
journal = "Entropy",
-
year = "2019",
-
volume = "21",
-
number = "11",
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "1099-4300",
-
URL = "https://www.mdpi.com/1099-4300/21/11/1123",
-
DOI = "doi:10.3390/e21111123",
-
abstract = "Recently, convolutional neural network (CNN) based on
the encoder-decoder structure have been successfully
applied to image dehazing. However, these CNN based
dehazing methods have two limitations: First, these
dehazing models are large in size with enormous
parameters, which not only consumes much GPU memory,
but also is hard to train from scratch. Second, these
models, which ignore the structural information at
different resolutions of intermediate layers, cannot
capture informative texture and edge information for
dehazing by stacking more layers. In this paper, we
propose a light-weight end-to-end network named the
residual dense pyramid network (RDPN) to address the
above problems. To exploit the structural information
at different resolutions of intermediate layers fully,
a new residual dense pyramid (RDP) is proposed as a
building block. By introducing a dense information
fusion layer and the residual learning module, the RDP
can maximize the information flow and extract local
features. Furthermore, the RDP further learns the
structural information from intermediate layers via a
multiscale pyramid fusion mechanism. To reduce the
number of network parameters and to ease the training
process, we use one RDP in the encoder and two RDPs in
the decoder, following a multilevel pyramid pooling
layer for incorporating global context features before
estimating the final result. The extensive experimental
results on a synthetic dataset and real-world images
demonstrate that the new RDPN achieves favourable
performance compared with some state-of-the-art
methods, e.g., the recent densely connected pyramid
dehazing network, the all-in-one dehazing network, the
enhanced pix2pix dehazing network, pixel-based alpha
blending, artificial multi-exposure image fusions and
the genetic programming estimator, in terms of
accuracy, run time and number of parameters. To be
specific, RDPN outperforms all of the above methods in
terms of PSNR by at least 4.25 dB. The run time of the
proposed method is 0.021 s, and the number of
parameters is 1,534,799, only 6percent of that used by
the densely connected pyramid dehazing network.",
-
notes = "also known as \cite{e21111123}",
- }
Genetic Programming entries for
Shibai Yin
Yibin Wang
Herbert Yang
Citations