基于注意力特征增强的投影图像光度补偿方法

目前,投影补偿算法已经取得了良好的研究成果,但大部分的投影图像色彩补偿研究,忽略了色彩传递函数建模过程中的光学部分,导致对色彩传递函数的建模精确度不高。同时,针对投影图像色彩补偿过程中,出现的加深网络导致提取特征信息丢失的现象,大多数的深度学习网络优化设计较少。基于以上问题,本文提出了一种基于注意力特征增强的投影图像光度补偿方法。该方法通过增加网络深度来提取带有彩色纹理投影表面的特征信息,同时采用深度学习来拟合复杂的复合辐射传递函数,以解决传统光度补偿方法存在的问题,提高了投影图像的质量和色彩,进一步消除了对高质量投影幕的依赖。本文所提方法的投影图像光度补偿结果,在峰值信噪比(PSNR)、均方根误差(RMSE)和结构相似性(SSIM)三项评价指标上,均好于其他对比算法,与CompenNet系列方法相比,本文所提方法在PSNR评价指标上最高提升5717、在RMSE评价指标上最高减少14968,在SSIM评价指标上最高提升2893。

Photometric compensation method for projection images based on attentional feature enhancement

At present,projection compensation algorithms have achieved good research results,but most of the projected image color compensation research ignores the optical part of the color transfer function modeling process,resulting in poor modeling accuracy of the color transfer function.At the same time,most of the deep learning network optimization designs are less for the phenomenon of deepening the network resulting in the loss of extracted feature information in the process of projected image colour compensation.To address the above problems,a luminosity compensation method for projected images based on attentional feature enhancement is proposed in this paper.The method extracts feature information from the projected surfaces with colored textures by increasing the depth of the network,and employs deep learning to fit a complex composite radiative transfer function to solve the problems of traditional photometric compensation methods,which improves the quality and colors of the projected images,and further eliminates the reliance on high quality projection screens.The luminosity compensation results of the proposed method in this paper are better than other comparative algorithms in three evaluation indexes,Peak Signal to Noise Ratio (PSNR),Root Mean Square Error (RMSE) and Structural Similarity Index Measure (SSIM).Compared with the CompenNet series of methods,the proposed method in this paper improves up to 5.717% in PSNR evaluation metrics,reduces up to 14.968% in RMSE evaluation metrics,and improves up to 2.893% in SSIM evaluation metrics.