基于残余插值的卷积神经网络去马赛克算法

• Published in: Nanjing Xinxi Gongcheng Daxue Xuebao Vol. 9; no. 6; pp. 650 - 655
• Main Authors: Jia, Huimiao, Li, Chunping, Zhou, Dengwen
• Format: Journal Article
• Language: Chinese
• Published: Nanjing Nanjing University of Information Science & Technology 01.12.2017; 华北电力大学 控制与计算机工程学院,北京,102206
• Subjects: Algorithms; Artificial neural networks; Interpolation; Neural networks; Run time (computers); Visual effects; 残余插值; BayerCFA模式; 卷积神经网络; 去马赛克; 边缘检测; edge detecting; residual interpolation; demosaicing; convolutional neural networks; Bayer CFA
• ISSN: 1674-7070
• DOI: 10.13878/j.cnki.jnuist.2017.06.009

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 PSNK and shorter running time compared with those of commonly used Bayer demosaicing algorithms.