01461nas a2200253   4500000000100000000000100001008004100002260001200043653001900055653003200074653003500106653003400141653001800175100001700193700001600210700001800226700001500244245005400259856008100313300001000394490000600404520078300410022001401193       2022                            d  c09/202210aImage Dehazing10aEncoder and Decoder Network10aGenerative Adversarial Network10aMulti-Scale Convolution Block10aLoss Function1 aHongqi Zhang1 aYixiong Wei1 aHongqiao Zhou1 aQianhao Wu00aED-Dehaze Net: Encoder and Decoder Dehaze Network  uhttps://www.ijimai.org/journal/sites/default/files/2022-08/ijimai_7_5_11.pdf  a93-990 v73 aThe 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.  a1989-1660