Sundari K Sivagami, Keerthi B Srutha
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, Chennai, 600127, India.
Sci Rep. 2024 Nov 29;14(1):29679. doi: 10.1038/s41598-024-80605-w.
Enhancing low-light images is crucial for various applications in computer vision, yet current approaches often fall short in balancing image quality and detail preservation. This study introduces a novel method designed to enhance low-light images by applying advanced mathematical techniques from geometric function theory. Specifically, we employ Sakaguchi-type class functions, subordinated with the Gegenbeur polynomial, to derive coefficient estimations. These estimations are then used in convolution kernels to produce enhanced image versions. The method was tested on the LOw-Light dataset (LOL), containing challenging low-light images with noise and artifacts. Our approach's effectiveness is validated through quantitative metrics, including PSNR and SSIM, as well as visual comparisons. The results demonstrate significant improvements over existing state-of-the-art methods, offering better visibility and detail retention. This method holds promise for enhancing images in critical fields such as surveillance and medical imaging.
增强低光图像对于计算机视觉中的各种应用至关重要，但目前的方法在平衡图像质量和细节保留方面往往存在不足。本研究引入了一种新颖的方法，旨在通过应用几何函数理论中的先进数学技术来增强低光图像。具体而言，我们采用与盖根堡多项式从属的坂口型类函数来推导系数估计。然后将这些估计用于卷积核以生成增强后的图像版本。该方法在包含具有噪声和伪影的具有挑战性的低光图像的低光数据集（LOL）上进行了测试。我们方法的有效性通过包括PSNR和SSIM在内的定量指标以及视觉比较得到了验证。结果表明，与现有的最先进方法相比有显著改进，具有更好的可见性和细节保留能力。该方法在监控和医学成像等关键领域的图像增强方面具有前景。
