改进融合策略下透明度引导的逆光图像增强  

An improved fusion strategy based on transparency-guided backlit image enhancement

作　　者：赵明华[1,2] 程丹妮 都双丽 胡静 石程 石争浩 Zhao Minghua;Cheng Danni;Du Shuangli;Hu Jing;Shi Cheng;Shi Zhenghao(School of Computer Science and Engineering,Xi′an University of Technology,Xi′an 710048,China;Shaanxi Key Laboratory of Network Computing and Security Technology,Xi′an 710048,China)

机构地区：[1]西安理工大学计算机科学与工程学院,西安710048 [2]陕西省网络计算与安全技术重点实验室,西安710048

出　　处：《中国图象图形学报》2022年第5期1554-1564,共11页Journal of Image and Graphics

基　　金：国家重点研发计划资助(2017YFB1402103-3);国家自然科学基金项目(61901363,61901362);陕西省自然科学基金项目(2020JQ-648,2019JM-381,2019JQ-729);陕西省教育厅重点实验室基金项目(20JS086)。

摘　　要：目的针对传统的逆光图像增强算法存在的曝光正常区域与逆光区域间阈值计算复杂、分割精度不足、过度曝光以及增强不足等问题,提出一种改进融合策略下透明度引导的逆光图像增强算法。方法对逆光图像在HSV(hue,saturation,value)空间中的亮度分量进行亮度提升和对比度增强,然后通过金字塔融合策略对改进的亮度分量进行分解和重构,恢复逆光区域的细节和颜色信息。此外,利用深度抠图网络计算透明度蒙版,对增强的逆光区域与源图像进行融合处理,维持非逆光区域亮度不变。通过改进融合策略增强的图像在透明度引导下既有效恢复了逆光区域又避免了曝光过度的问题。结果实验在多幅逆光图像上与直方图均衡算法、MSR(multi-scale Retinex)、Zero-DEC(zero-reference deep curve estimation)、AGLLNet(attention guided low-light image enhancement)和LBR(learning-based restoration)5种方法进行了比较,在信息熵(information entropy,IE)和盲图像质量指标(blind image quality indicators,BIQI)上,比AGLLNet分别提高了1.9%和10.2%;在自然图像质量评价(natural image quality evaluation,NIQE)方面,比Zero-DCE(zero-reference deep curve estimation)提高了3.5%。从主观评估上看,本文算法增强的图像在亮度、对比度、颜色及细节上恢复得更加自然,达到了较好的视觉效果。结论本文方法通过结合金字塔融合技术与抠图技术,解决了其他方法存在的色彩失真和曝光过度问题,具有更好的增强效果。ObjectiveThe backlit image is a kind of redundant reflection derived of the light straightforward to the camera,resulting in dramatic reduced visibility of region of interest(ROI)in the captured image.Different from ordinary low-light images,the backlit image has a wider grayscale range due to the extremely dark and bright parts.Traditional enhancement algorithms restore brightness and details of backlit parts in terms of overexposure and color distortion.Fusion technology or threshold segmentation is difficult to implement sufficient enhancement or adequate segmentation accuracy due to uneven images gray distribution.A transparency-guided backlit image enhancement method is demonstrated based on an improved fusion strategy.MethodThe backlit image enhancing challenge is to segment and restore the backlit region,which is regarded as the foreground and the rest as the background.First,the deep image matting model like encoder-decoder network and refinement network is illustrated.The backlit image and its related trimap are input into the encoder-decoder network to get the preliminary transparency value matrix.The output is melted into the refinement network to calculate the transparency value of each pixel,which constitutes the same scale alpha matte as the original image.The range of transparency value is between 0 and 1,0 and 1 indicates pixels in the normal exposure region and the backlit region,respectively.The value between 0 and 1 is targeted to the overlapped regions.The alpha matte can be used to substitute the traditional weight map for subsequent fusion processing in terms of processed non-zero pixels.Next,the backlit image is converted into HSV(hue,saturation,value)space to extract the luminance component and the adaptive logarithmic transformation is conducted to enhance brightness in terms of the base value obtained from the number of low-gray image pixels.Contrast-limited adaptive histogram equalization is also adopted to enhance the contrast of the luminance component while logarithmic transformation

关 键 词：图像处理 逆光图像增强 深度抠图 灰度拉伸 金字塔融合 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]