WaveLiteDehaze-Network: A Low-Parameter Wavelet-Based Method for Real-Time Dehazing

Abstract

Although the image dehazing problem has received considerable attention over recent years, the existing models often prioritise performance at the expense of complexity, making them unsuitable for real-world applications, which require algorithms to be deployed on resource constrained-devices. To address this challenge, we propose WaveLiteDehaze-Network (WLD-Net), an end-to-end dehazing model that delivers performance comparable to complex models while operating in real time and using significantly fewer parameters. This approach capitalises on the insight that haze predominantly affects low-frequency information. By exclusively processing the image in the frequency domain using discrete wavelet transform (DWT), we segregate the image into high and low frequencies and process them separately. This allows us to preserve high-frequency details and recover low-frequency components affected by haze, distinguishing our method from existing approaches that use spatial domain processing as the backbone, with DWT serving as an auxiliary component. DWT is applied at multiple levels for better information retention while also accelerating computation by downsampling feature maps. Subsequently, a learning-based fusion mechanism reintegrates the processed frequencies to reconstruct the dehazed image. Experiments show that WLD-Net outperforms other low-parameter models on real-world hazy images and rivals much larger models, achieving the highest PSNR and SSIM scores on the O-Haze dataset. Qualitatively, the proposed method demonstrates its effectiveness in handling a diverse range of haze types, delivering visually pleasing results and robust performance, while also generalising well across different scenarios. With only 0.385 million parameters (more than 100 times smaller than comparable dehazing methods), WLD-Net processes 1024 × 1024 images in just 0.045 s, highlighting its applicability across various real-world scenarios. The code is available at https://github.com/AliMurtaza29/WLD-Net.