HDR image processing algorithm for portrait based on multi feature fusion
Deep learning based high dynamic range(HDR)image processing algorithms has the problem of skin color deviation when processing images containing human figures.In response to this issue,this article proposes a portrait HDR image processing algorithm based on multi feature fusion-U2HDRnet.This algorithm consists of three parts:skin feature extraction module,trilateral feature extraction module and color reconstruction module.Firstly,the skin feature extraction module separates the color and position information of the skin region.Secondly,the trilateral feature extraction module extracts local features,global features and semantic features of the image,and fuses them with skin features.Finally,the color reconstruction module interpolates the grid in terms of space and color depth.In addition,this article adds an improved fusion module of self attention and convolution to improve the processing performance of HDR.At the same time,this article also produces the PortraitHDR dataset for portraits,filling the gap in the dataset in this field.The test results show that the PSNR of U2HDRnet reaches 31.42 dB,and the SSIM reaches 0.985,both of which are superior to the commonly used HDR algorithms.They obtain high-quality portrait HDR images while avoiding skin distortion.
deep learninghigh dynamic rangeskin feature extractionattention mechanismcolor reconstruction
吴春林、张永爱、林志贤、郭太良、林鹏飞、林坚普
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福州大学 先进制造学院,福建 泉州 362200
福州大学 物理与信息工程学院,福建 福州 350108
东京大学 信息科学技术学院,日本 东京 113-8657
深度学习 高动态范围 皮肤特征提取 注意力机制 色彩重建
国家重点研发计划福建省自然科学基金福建省教育厅中青年教师教育科研项目National Key R&D Program of China