HDNet-LE: A Hybrid Deep-Net Architecture for Low Light Image Enhancement

In this paper, we propose a novel approach, HDNet-LE, designed to enhance low-light (LL) images in terms of contrast, noise removal, and other degradation kinds by leveraging the power of Wavelet transform (WT) and Fourier Transform (FT). HDNet-LE combines the strengths of Generative Adversarial Networks (GAN) with the multi-scale analysis capabilities of the Wavelet transform and the spatial frequency domain through the FT. Experimental results demonstrate the effectiveness of the proposed method in improving the visibility and quality of LL images. The superiority of the proposed method is substantiated through a comprehensive performance evaluation using objective quality metrics criteria.