【摘要】 近年来，微光图像增强技术备受关注，但仍存在一些问题。例如，有时暗区域没有完全改善，有时光源或光源附近的明亮区域曝光过度。针对以上问题，提出了一种基于Retinex模型里照度分量和反射分量双增强的图像增强方法。该方法首先将原始图像由RGB空间转换到HSV空间，并提取其中的V分量进行后续处理；然后对V分量进行引导滤波获得图像照度分量，根据Retinex理论通过分解得到图像的反射分量；接着对照度分量进行全局自适应亮度增强，对反射分量进行多尺度细节加强；再将增强后的照度分量和反射分量按照Retinex模型重构得到V分量重构图，并经过非线性变换处理和局部对比度增强处理；最终转换回RGB空间获得最终增强图。实验结果表明，该方法的峰值信噪比(peak signal-to-noise ratio,PSNR)和结构相似指数(structural similarity index measure,SSIM)评价值分别为17.741和0.765，具有更好的图像质量，较其他方法表现出更好的增强效果。更多还原

【Abstract】 In recent years, low-level-light image enhancement technology has attracted much attention, but there are still some problems. For example, sometimes dark areas are not completely improved, and sometimes bright areas near the light source are overexposed. In response to the above issues, an image enhancement method based on Retinex model with dual enhancement of illumination and reflection components was proposed. Firstly, the original image was transformed from RGB space to HSV space, and the V-component was extracted for subsequent processing. Then, the V-component was filtered to obtain the illumination component of the image, and the reflection component of the image was obtained by decomposition according to Retinex theory. Next, the global adaptive brightness enhancement was applied to the illumination component, and the multi-scale detail enhancement was applied to the reflection component. Then, the enhanced illumination component and reflection component were reconstructed according to Retinex model to obtain the V-component reconstruction image, which was processed by nonlinear transformation and local contrast enhancement. Finally, it was converted back to RGB space to obtain the final enhanced image. The experimental results show that the evaluation values of peak signal-to-noise ratio(PSNR) and structural similarity index measure(SSIM) of this method are 17.741 and 0.765, respectively, which have better image quality and better enhancement effect than other methods.更多还原