  Most existing dehazing algorithms often use hand-crafted features or
Convolutional Neural Networks (CNN)-based methods to generate clear images
using pixel-level Mean Square Error (MSE) loss. The generated images generally
have better visual appeal, but not always have better performance for
high-level vision tasks, e.g. image classification. In this paper, we
investigate a new point of view in addressing this problem. Instead of focusing
only on achieving good quantitative performance on pixel-based metrics such as
Peak Signal to Noise Ratio (PSNR), we also ensure that the dehazed image itself
does not degrade the performance of the high-level vision tasks such as image
classification. To this end, we present an unified CNN architecture that
includes three parts: a dehazing sub-network (DNet), a classification-driven
Conditional Generative Adversarial Networks sub-network (CCGAN) and a
classification sub-network (CNet) related to image classification, which has
better performance both on visual appeal and image classification. We conduct
comprehensive experiments on two challenging benchmark datasets for
fine-grained and object classification: CUB-200-2011 and Caltech-256.
Experimental results demonstrate that the proposed method outperforms many
recent state-of-the-art single image dehazing methods in terms of image
dehazing metrics and classification accuracy.
