We introduce NeRD, a new demosaicking method for generating full-color images from Bayer patterns. Our approach leverages advancements in neural fields to perform demosaicking by representing an image as a coordinate-based neural network with sine activation functions. The inputs to the network are spatial coordinates and a low-resolution Bayer pattern, while the outputs are the corresponding RGB values. An encoder network, which is a blend of ResNet and U-net, enhances the implicit neural representation of the image to improve its quality and ensure spatial consistency through prior learning. Our experimental results demonstrate that NeRD outperforms traditional and state-of-the-art CNN-based methods and significantly closes the gap to transformer-based methods.

翻译：摘要：我们提出NeRD，一种从拜耳模式生成全彩图像的新型去马赛克方法。该方法利用神经场的最新进展，将图像表示为带有正弦激活函数的基于坐标的神经网络，从而实现去马赛克。网络输入为空间坐标和低分辨率拜耳模式，输出为对应的RGB值。编码器网络融合了ResNet与U-net架构，通过先验学习增强图像的隐式神经表示，提升图像质量并确保空间一致性。实验结果表明，NeRD性能优于传统及最先进的基于CNN的方法，并显著缩小了与基于Transformer方法的差距。