基于深度学习的复杂退化模糊图像恢复

项目名称： 基于深度学习的复杂退化模糊图像恢复

项目编号： No.61503250

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 陈晓钢

作者单位： 上海理工大学

项目金额： 22万元

中文摘要： 实际模糊图像通常受多类退化因素共同影响，例如图像压缩失真和图像噪声，这些退化因素会使传统卷积模型无法准确估计卷积核并降低图像恢复品质。本项目研究基于深度学习的方法估计卷积核，再恢复出清晰图像。该项目创新点包括：基于深度信念网络和降噪自动编码机的深度学习方法估计卷积核，抑制噪声和非线性退化因素对卷积核估计的影响。其次，研究基于受限玻尔兹曼机的清晰图像先验特征学习，用无监督学习方法获得先验特征，并将其用于约束反卷积算法。本项目在提高实际模糊图像卷积核估计和去模糊算法的鲁棒性方面做出贡献。

中文关键词： 图像恢复；图像去模糊；深度学习

英文摘要： Real blurred images usually degraded with certain nonlinear effects, such as image lossy compression and image noises. Such nonlinear factors could reduce the quality of the blurring kernel estimation and also make the traditional deblurring model less reliable. To effectively improve the deblurring quality, we investigate the deep network to estimate the convolution kernel followed by latent image estimation. There are two contributions in our work. The first is the kernel estimation based on Deep Believe Network. We employ the Denoising Autoencoder to pretrain the network thus the robustness of the kernel estimation is improved. The second is the sharp images' Priors learning based on Restricted Boltzmann Machine. We use unsupervised learning to fit the image patch distribution. Then the learned Priors are effectively incorported into the deblurring model. The program would make contribtions in improving the robustness of the image deblurring system.

英文关键词： Image Restoration;Image Deblur;Deep Learning