TY - JOUR
KW - Image Dehazing
KW - Encoder and Decoder Network
KW - Generative Adversarial Network
KW - Multi-Scale Convolution Block
KW - Loss Function
AU - Hongqi Zhang
AU - Yixiong Wei
AU - Hongqiao Zhou
AU - Qianhao Wu
AB - 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.
IS - Special Issue on Multimedia Streaming and Processing in Internet of Things with Edge Intelligence
M1 - 5
N2 - 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.
PY - 2022
SP - 93
EP - 99
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - ED-Dehaze Net: Encoder and Decoder Dehaze Network
UR - https://www.ijimai.org/journal/sites/default/files/2022-08/ijimai_7_5_11.pdf
VL - 7
SN - 1989-1660
ER -