PT Unknown
AU Patricia Suarez
   Angel Sappa
   Boris X. Vintimilla
TI Cross-spectral image dehaze through a dense stacked conditional GAN based approach
BT 14th IEEE International Conference on Signal Image Technology & Internet Based System
PY 2018
DE Infrared imaging; Dense; Stacked CGAN; Crossspectral; Convolutional networks
AB This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implementedreceives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colorsand the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results.
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