Multiple-feature fusion based generative adversarial network for image dehazing

Abstract

To enhance image clarity and address the difficulties in feature extraction and incomplete haze removal in traditional image dehazing processes, a multi-feature fusion based generative adversarial dehazing network is proposed.The network adopts a generative adversarial approach and consists of a generator and a discriminator.The generator utilizes an encoderdecoder structure, and extracts haze-related feature maps from multiple receptive fields by a multi-feature extraction fusion (MFEF) block.The discriminator uses a series of convolutional calculations to analyze the feature differences between the generated images and the clear images, guiding the generator to output move realistic dehazing images.The experimental images show that the proposed method can effectively eliminate haze interference while preserving the original color tone of the image to the greatest extent possible.The experimental results demonstrate that the dehazed images produced by our algorithm have improved peak signal-to-noise ratio and structural similarity with an average of 2.588 dB and 2.66% respectively, compared with existing methods.

Keywords

• image processing;image dehazing;deep learning;generative adversarial;multiple feature fusion