ED-Dehaze Net: Encoder and Decoder Dehaze Network - Zhang, Hongqi; Wei, Yixiong; Zhou, Hongqiao; Wu, Qianhao (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 2022)
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.
Keywords: image dehazing; encoder and decoder network; generative adversarial etwork; multi-scale convolution block; loss function; IJIMAI