Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (24): 219-226.

• Graphics and Image Processing •

### Multi-scale Image Deblurring Model with DenseNet

Multi-scale Image Deblurring Model with DenseNet

1. School of Software, Liaoning Technical University, Huludao, Liaoning 125105, China
• Online:2021-12-15 Published:2021-12-13

### 融合DenseNet的多尺度图像去模糊模型

1. 辽宁工程技术大学 软件学院，辽宁 葫芦岛 125105

Abstract:

Multi-scale convolutional neural network is widely used in the field of image deblurring, but the method of setting network parameters independently at different scales will lead to difficult network training, and lead to problems such as too large parameters, decreased stability and unconstrained solution space. According to the above problems of multi-scale algorithm, an image deblurring algorithm that shares network weight across scales and fuses DenseNet is proposed. In this model, encoder-decoder structure is adopted, and dense blocks are introduced to improve the structure, it can obtain the in-depth characteristic information to the greatest extent. At the same time, the method of sharing network weight across scales is proposed to share parameters in the process of scale iteration, which significantly reduces the training difficulty and significantly improves the stability. The advantages are two fold. Experiments are carried out on GOPRO and Kohler of large scale moving image deblurring data set and blind deblurring data set of image. The results show that the model is superior to the existing methods qualitatively and quantitatively, and superior to other algorithms in both subjective vision and experimental data. Compared with other methods in this field in recent years, this method has the characteristics of simpler network structure, fewer parameters and easier training. The proposed algorithm performs well in subjective and objective evaluation, and can deal with a variety of fuzzy kernels, and has strong robustness, which can be applied to deblurring moving images.