UNASSIGNED: We introduce an optimized deep CNN designed for demosaicing both color and NIR images obtained using a hexachromatic imaging sensor.
UNASSIGNED: A residual CNN was fine-tuned and trained on a dataset of color images and subsequently assessed on a series of dual-channel, color, and NIR images to demonstrate its enhanced performance compared with traditional bilinear interpolation.
UNASSIGNED: Our optimized CNN for demosaicing color and NIR images achieves a reduction in the mean square error by 37% for color and 40% for NIR, respectively, and enhances the structural dissimilarity index by 37% across both imaging modalities in pre-clinical data. In clinical datasets, the network improves the mean square error by 35% in color images and 42% in NIR images while enhancing the structural dissimilarity index by 39% in both imaging modalities.
UNASSIGNED: We showcase enhancements in image resolution for both color and NIR modalities through the use of an optimized CNN tailored for a hexachromatic image sensor. With the ongoing advancements in graphics card computational power, our approach delivers significant improvements in resolution that are feasible for real-time execution in surgical environments.
■我们引入了一种优化的深度CNN,旨在对使用六色成像传感器获得的彩色和近红外图像进行去马赛克。
■在彩色图像数据集上对残差CNN进行了微调和训练,随后在一系列双通道上进行了评估,颜色,和近红外图像,以证明其与传统双线性插值相比的增强性能。
■我们针对彩色和NIR图像进行去马赛克的优化CNN将彩色和NIR的均方误差降低了37%,将NIR的均方误差降低了40%,分别,在临床前数据中,两种成像方式的结构差异指数均提高了37%。在临床数据集中,该网络在彩色图像中将均方误差提高了35%,在NIR图像中将均方误差提高了42%,同时在两种成像方式中将结构相异指数提高了39%。
■我们通过使用为六色图像传感器量身定制的优化CNN,展示了颜色和NIR模式的图像分辨率增强。随着显卡计算能力的不断提高,我们的方法显著提高了分辨率,这对于在手术环境中实时执行是可行的.