Research On Content Aware Ultra-High Defination Automatic Portrait Image Enhancement Method

In the field of image enhancement,post-processing of ultra-high-definition(UHD)images mainly relies on manual work.Methods based on deep learning can only handle low-resolution images,limiting their application range,and they also lack relevant high-quality data samples.To address these issues,this study proposes a deep learning method based on scene content awareness for automatic enhancement of UHD portraits.The main contributions are as follows:(1)To solve the problem of the scarcity of UHD(4k and above)portrait datasets,a largescale paired dataset of portrait images captured by digital SLR cameras was created.The overall dataset includes six types of portrait data,including five single-scene data and mixed-scene data,serving as the standard experimental dataset for this study.(2)A lightweight curve-based method(ISP-Curve)was proposed to solve the problems that conventional deep learning-based image enhancement methods cannot process UHD images properly.First,an ISP branch based on cross-attention mechanism is used for global adaptive brightness and contrast adjustment.Then,a CNN network is used to extract highdimensional feature vectors from the input low-resolution images for curve encoding of channel and position information,constructing a pixel-level curve mapping relationship based on image content adaptation to achieve portrait enhancement of UHD images.(3)An improved three-dimensional color lookup table fusion method(ST-LUT)based on image content features was proposed.To address the poor modeling capabilities of the curvebased method in mixed and complex scenes,Swin Transformer network with window multihead attention mechanism is used as the backbone network to better extract global feature information.Through weighted fusion,a scene-adaptive lookup table is obtained to enhance the original image.A person area mask is used as a regularization term to prioritize the importance of person area and improve the image enhancement performance in mixed scenes.(4)The proposed UHD image enhancement model was deployed to a server using Python and Qt environments to establish an automatic portrait modification platform.The proposed ISP-Curve and ST-LUT methods were experimentally and comparatively analyzed against mainstream methods on the publicly available datasets MIT-Adobe Five K,PPR10 K,and the proposed UHD real-scene human portrait dataset.Experimental results show that both methods can process UHD images at expected speeds,achieving enhancement effects highly consistent with target reference images.