ED-Dehaze Net: Encoder and Decoder Dehaze Network.

Hongqi Zhang; Yixiong Wei; Hongqiao Zhou; Qianhao Wu

doi:10.9781/ijimai.2022.08.008

Authors

Hongqi Zhang Research Institute of CETC (China).
Yixiong Wei Research Institute of CETC (China).
Hongqiao Zhou Research Institute of CETC (China).
Qianhao Wu Research Institute of CETC (China).

DOI:

https://doi.org/10.9781/ijimai.2022.08.008

Keywords:

Image Dehazing, Encoder and Decoder Network, Generative Adversarial Network, Multi-Scale Convolution Block, Loss Function

Supporting Agencies

We are very grateful to Dr Jun Zhang and Dr Peng Chen, professors in Anhui University, for their invaluable helps to develop the proposed method and prepare the original manuscript.

Abstract

The presence of haze will significantly reduce the quality of images, such as resulting in lower contrast and blurry details. This paper proposes a novel end-to-end dehazing method, called Encoder and Decoder Dehaze Network (ED-Dehaze Net), which contains a Generator and a Discriminator. In particular, the Generator uses an Encoder-Decoder structure to effectively extract the texture and semantic features of hazy images. Between the Encoder and Decoder we use Multi-Scale Convolution Block (MSCB) to enhance the process of feature extraction. The proposed ED-Dehaze Net is trained by combining Adversarial Loss, Perceptual Loss and Smooth L1 Loss. Quantitative and qualitative experimental results showed that our method can obtain the state-of-the-art dehazing performance.

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