双分支残差去马赛克网络

为了缓解传统拜耳型去马赛算法中常出现的拉链和伪影等问题,提出一个新颖的基于深度学习的去马赛克算法。所提算法首先对马赛克图像中的红色、绿色及蓝色通道中的像素进行分解、剔除及组合等操作得到两幅彩色图像,然后将这两幅彩色图像输入到设计的卷积神经网络中,以重建出完整的彩色图像,该网络能充分地利用卷积层所生成的特征信息。实验结果表明,所提算法重建出的完整彩色图像的质量相对较高,并且在一定程度上缓解了拉链和伪影等问题,其客观指标和主观评价都优于对比算法。

Double Branch Residual Network for Demosaicing

A novel demosaicing algorithm based on deep learning is proposed to address the problems of zippers and artifacts that often occur in the traditional Bayer demosaicing algorithm.First,the proposed algorithm decomposes,removes,and combines pixels in the red,green,and blue channels of mosaic images to obtain two color images.The two color images are then inputted into a designed convolutional neural network to reconstruct the complete color image.The network can make the full use of the feature information generated by the convolutional layer.The experimental results show that the quality of the whole color image reconstructed by the proposed algorithm is relatively high,and the zippers and artifacts are relieved to a certain extent.The objective index and subjective evaluation of the proposed algorithm are better than the contrast algorithms.