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 6% of that
used by the densely connected pyramid dehazing network.