Infra Aerial Dehaze: Dehazing of Infrared Aerial Images Using Cycle Generative Adversarial Network

Abstract

Hazy environments create substantial difficulties in infrared (IR) aerial imaging by reducing the quality of image making it difficult for human eye to see the object features clearly. This work proposes a robust CycleGAN based haze removal model, called Infra Aerial Dehaze, specifically for infrared aerial images with good generalization to various haze densities. The proposed model is implemented using the encoder-decoder-transformer based dehazing architecture and its performance is tested using two datasets HIT-UAV and Transpetro-Train containing various scenarios and hazy conditions. Moreover, a new dataset of 3604 aerial infrared hazy images is synthetically produced by an atmospheric scattering model to further validate the proposed method. The performance of the proposed dehazing model is assessed using qualitative and quantitative measures, with PSNR and SSIM metrics used for quantitative assessment. Additionally, CycleGAN was not previously used for infrared image dehazing, therefore we present first CycleGAN-based generative model, Infra Aerial Dehaze which can remove haze from infrared aerial images with varying haze intensities. Experimental results confirm that the proposed model is capable of restoring details of the texture and enhancing the visibility of the image. The proposed model can be applied effectively for dense haze removal and demonstrate superior results to other prior based dehazing techniques.