基于残余插值的卷积神经网络去马赛克算法

Demosaicing based on residual interpolation and convolutional neural networks

在去马赛克问题中,为了精确插值倾斜边缘并提高结果图像的整体质量,提出一种基于残余插值的卷积神经网络去马赛克算法.针对Bayer格式的颜色滤波阵列,插值绿色平面时,对于红蓝通道信息不全的问题,采用同通道邻近像素值近似代替,综合考虑3个通道的梯度,运用倾斜方向的边缘检测算子,将倾斜边缘分为不同方向的边缘分别插值.在插值完成后,利用深度卷积神经网络,进一步训练插值结果.在标准的IMAX数据集上,与目前流行的算法相比,本文算法视觉上更接近原图,具有更高的峰值信噪比和更短的运行时间.

In order to accurately restore the texture on the oblique edges and improve the overall resolution of the demosaiced image,a convolutional neural network demosaicing algorithm is proposed based on residual interpolation.The algorithm uses the information of Bayer color filter arrays to calculate the gradient of diagonal edges,which can be used to determine the edge directions. Therefore,the corresponding interpolation formula is proposed for different edges.We incorporate the convolutional neural networks into our method to refine the interpolated images. To demonstrate the superiority of the proposed algorithm,several experiments were conducted with IMAX dataset.The experimental results show that the proposed algorithm exhibits better visual effect,higher PSNR and shorter running time compared with those of commonly used Bayer demosaicing algorithms.