亮度均衡与边缘强化的多尺度低照度图像增强算法

Multiscale Low-Light Image Enhancement Algorithm with Brightness Equalization and Edge Enhancement Algorithm

为解决现有低照度图像增强算法存在的细节信息丢失、伪影和自然度较差等问题,提出一种亮度均衡与边缘强化的多尺度低照度图像增强算法。首先,利用改进的Sobel算子提取边缘细节,得到边缘细节增强图像;然后,对图像HSV颜色空间亮度V进行Retinex增强,并用改进的Gamma校正进行亮度均衡化处理,得到亮度均衡图像;接下来,计算边缘细节增强图像和亮度均衡图像的拉普拉斯权重图、显著性权重图和饱和度权重图,进而得到归一化权重图;最后,将归一化权重图分解成高斯金字塔,将边缘细节增强图像和亮度均衡图像分解成拉普拉斯金字塔,采用多尺度金字塔融合策略进行图像融合,得到最终增强图像。实验结果表明,所提算法在LOL数据集上的峰值信噪比、结构相似性和自然度图像质量评估器的平均值均优于其他算法,且能够有效提升低照度图像的对比度、清晰度,增强后的图像细节信息更丰富、色彩饱和度更好,图像质量提升明显。

To address issues such as detail loss,artifacts,and unnatural appearance associated with current low-illumination image enhancement algorithms,a multiscale low-illumination image enhancement algorithm based on brightness equalization and edge enhancement is proposed in this study.Initially,an improved Sobel operator is employed to extract edge details,yielding an image with enhanced edge details.Subsequently,the brightness component(V)of the HSV color space is enhanced using Retinex,and brightness equalization is accomplished via improved Gamma correction,yielding an image with balanced brightness.The Laplacian weight graph,significance weight graph,and saturation weight graph are computed for the edge detail-enhanced image and brightness-balanced image,culminating in the generation of a normalized weight graph.This graph is then decomposed into a Gaussian pyramid,while the edge detail-enhanced image and brightness-balanced image are decomposed into a Laplacian pyramid.Finally,a multiscale pyramid fusion strategy is employed to merge the images,resulting in the final enhanced image.Experimental results demonstrate that the proposed algorithm outperforms existing algorithms on the LOL dataset in terms of average peak signal to noise ratio,structural similarity,and naturalness image quality evaluator.This algorithm effectively enhances the contrast and clarity of low-illumination images,resulting in images with richer detail information,improved color saturation,and considerably enhanced quality.