FrTrGAN: Single image dehazing using the frequency component of transmission maps in the generative adversarial network

Abstract

Hazy images, particularly in outdoor scenes, have reduced visibility due to atmospheric particles, making image dehazing a critical task for enhancing visual clarity. The main challenges in image dehazing involve accurately detecting and removing haze while preserving fine details and maintaining overall image quality. Many existing dehazing methods struggle with varying haze conditions, often compromising the structural and perceptual integrity of the restored images. In this paper, we introduce FrTrGAN, a framework for single-image dehazing that leverages the frequency components of transmission maps. This novel framework addresses these challenges by integrating the Fourier Transform within an enhanced CycleGAN architecture. Unlike traditional spatial-domain dehazing methods, FrTrGAN operates in the frequency domain, where it isolates low-frequency haze components – responsible for blurring fine details – and removes them more precisely. The Inverse Fourier Transform is then applied to map the refined data back to the spatial domain, ensuring that the resulting images maintain clarity, sharpness, and structural integrity. We evaluate our method on multiple datasets, including HSTS, SOTS Outdoor, O-Haze, I-Haze, D-Hazy, BeDDE and Dense-Haze using PSNR and SSIM for quantitative performance assessment. Additionally, we include results based on non-referential metrics such as FADE, SSEQ, BRISQUE and NIQE to further evaluate the perceptual quality of the dehazed images. The results demonstrate that FrTrGAN significantly outperforms existing methods while effectively restoring both frequency components and perceptual image quality. This comprehensive evaluation highlights the robustness of FrTrGAN in diverse haze conditions and underscores the effectiveness of a frequency-domain approach to image dehazing, laying the groundwork for future advancements in the field.