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LoLi-IEA: low-light image enhancement algorithm

Low-light image enhancement has posed a significant challenge in recent years due to non-uniform luminance in real-world images. Color restoration, luminance mapping, and estimation of the curve and levels are some techniques used by the algorithms to enhance the image. However, real-world images often have non-uniform luminance, requiring local enhancement in certain areas rather than global enhancement. In order to tackle this issue, this paper introduces a novel methodology based on deep learning, employing two convolutional network architectures. The first one classifies the brightness level of the input image, while the second one enhances the brightness level based on information obtained through the first architecture. To train this model, two commonly used datasets in state-of-the-art research are used: the LOL (Low-Light) and Synthetic Low-light. Both datasets contain low-light and ground-truth image pairs, which makes it possible to make a proper estimate between non-uniform and uniform luminosity. The proposed algorithm is applied over resized images from the UHD-LOL4k dataset, with a performance evaluation through the metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Evaluator (NIQE), and Blind /Unreferenced Image Spatial Quality Evaluator (BRISQUE). According to the results, the proposed method outperforms algorithms with more complex architectures in the literature. The double convolutional architecture emphasizes local enhancement in real-world scenes and global enhancement in images with very low luminosity. Overall, this paper presents a significant contribution to low-light image enhancement offering an effective solution to the challenges posed by non-uniform luminance in real-world images

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