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This paper develops a deep learning model, called Encoder-Recurrent Decoder Network (ERDN), which recovers the clear image from a degrade hazy image without using the atmospheric scattering model. The proposed model consists of two key components-an encoder and a decoder. The encoder is constructed by a residual efficient spatial pyramid (rESP) module such that it can effectively process hazy images at any resolution to extract relevant features at multiple contextual levels. The decoder has a recurrent module which sequentially aggregates encoded features from high levels to low levels to generate haze-free images. The network is trained end-to-end given pairs of hazy-clear images. Experimental results on the RESIDE-Standard dataset demonstrate that the proposed model achieves a competitive dehazing performance compared to the state-of-the-art methods in term of PSNR and SSIM.
|Title of host publication||2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - May 2020|
|Event||2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain|
Duration: 4 May 2020 → 8 May 2020
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020|
|Period||4/05/20 → 8/05/20|
- encoder-recurrent decoder network
- single image dehazing
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