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GANID: a novel generative adversarial network for image dehazing

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Abstract

This paper presents a novel algorithm to dehaze a given hazy input image using a generative adversarial network (GAN). The proposed GAN structure uses a Feature Residual Dense combined Network (FRDN) as a generator and a Markovian discriminator (PatchGAN) with additional layers as the discriminator. FRDN is capable of extracting contextual information using Feature Module (FM) in conjunction with the Residual Dense Module, and IMCU enhances collaborative learning which enhances its performance. The inclusion of proposed reality and visibility loss functions along with \(L_1\) loss improves the scene visibility and realness of the dehazed image. The network is trained with images from the benchmark datasets—RESIDE and NTIRE 2021. The proposed technique’s performance is evaluated using various metrics such as PSNR, SSIM, FSIM, FADE, NIQE, and BRISQUE. An average PSNR of 25.701, 32.52, and 33.96 has been obtained with the Indoor Training Set, Synthetic Objective Testing Set indoor, and SOTS outdoor images, respectively. The experimental results reveal that the suggested method’s performance is better compared to other state-of-the-art techniques.

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  1. Computer Vision Lab, Department of Electronics and Communication, College of Engineering, APJA K.T.U, Thiruvananthapuram, India

    Chippy M. Manu & K. G. Sreeni

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Manu, C.M., Sreeni, K.G. GANID: a novel generative adversarial network for image dehazing. Vis Comput 39, 3923–3936 (2023). https://doi.org/10.1007/s00371-022-02536-9

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