基于光照补偿的HSV空间多尺度Retinex图像增强

Multiscale Retinex Image Enhancement in HSV Space Based on Illumination Compensation

针对弱光环境下采集到的图像存在欠曝光的问题,提出了一种基于光照补偿的HSV空间多尺度Retinex图像增强算法。首先在HSV空间下将原始亮度分量同等代换为本征突出层和照明补偿层;然后采用改进的双边核函数(IBKF)处理本征突出层,并根据多尺度Retinex得到反射层;接着通过麻雀搜索优化双边伽马函数来处理照明补偿层,同时在麻雀搜索后期引入萤火虫扰动,使其更快地收敛到全局最优,并从亮度过渡层中提取光照层;最后融合反射层和光照层,并进行颜色空间转换,得到最终的输出图像。实验结果表明,所提算法改善了图像增强过程中的光照损失问题,提升了图像清晰度,获得了更好的视觉感知。

A multiscale Retinex image enhancement algorithm in HSV space based on illumination compensation is proposed to solve the under exposure of images acquired in low-light conditions. First, in HSV space, the intrinsic protrusion and illumination compensation layers both replace the original luminance component. Then, the intrinsic protrusion layer is processed by improved bilateral kernel function(IBKF), and the reflection layer is obtained according to multiscale Retinex. The illumination compensation layer is processed by optimizing the bilateral Gamma function through sparrow search, and firefly disturbance is introduced in the later stage of sparrow search to make it converge to the global optimum faster. The illumination layer is extracted from the brightness transition layer. Finally, the reflection and illumination layers are fused, and color space conversion is employed to generate the final output image. The experimental results show that the proposed algorithm improves the illumination loss in the process of image enhancement as well as image clarity and obtains better visual perception.