| 英文标题 | Image motion deblur algorithm based on motion information |
| 作者英文名 | Dong Xingyu, Liu Chuanqi, Zhao Jiankang |
| 机构英文名 | School of Electronic Information & Electrical Engineering,Shanghai Jiao Tong University,Shanghai 201100,China |
| 英文摘要 | The existing motion deblurring algorithm is difficult to effectively recover the composite motion blur with large rotational motion. This paper proposed a neural network framework based on U-net model for this problem. By combining motion information to the input of network, this framework gave different motion constraint to each pixel. Through the structure of encoder and decoder, each pixel obtained the prediction value, thereby the blurry image could be recovered in an end-to-end manner. The experiment was compared with the current state-of-art deblurring algorithm on the universal data set. This method improved the PSNR value of the best algorithm by 0.14 dB, and reduced the running time of the best real-time algorithm by 0.1 s. At the same time, it was verified on the test dataset with rotational motion. This proves that the algorithm obtains better restoration quality. |
| 英文关键词 | motion blur; image restoration; convolutional neural network; motion constraint |