Our paper of Image Processing and Intelligent systems (IPIS) Lab is published in IEEE Trans. Multimedia (TMM), which is one of the Top Journal in CS/EE/Multimedia (IF: 8.182). 

[LINK]

Title: 

Region-Based Dehazing via Dual-Supervised Triple-Convolutional Network

Authors:

Joongchol Shin, Hasil Park, Joonki Paik

Abstract:

Most physical model-based dehazing methods are subject to contrast degradation in a dark or shadow region because of the mismatch between the physical model and real haze. This degradation decreases the quality of dehazed images. Furthermore, the retinex-haze combined models can cause the brightness saturation problem in a haze region. For this reason, the retinex-haze combined approaches are not appropriate to enhance the real-world haze images. To solve these problems, we present a novel region-based dehazing method via dual-supervised triple-convolutional network (TCN). More specifically, the proposed network first simulates the mismatch problem based on the region-model. Next, we then train the proposed triple-convolutional network, which can estimate the degraded regions. We then present a novel dual-supervised learning method to efficiently train the networks using a non-ideal dataset. Experimental results show that the proposed method outperforms state-of-the-art approaches in solving complex haze problems. The output of the proposed network has a high-similarity index in most cases for various benchmark dataset. Our approach also produces high-quality images in real haze image datasets.