Ghost-Unet: multi-stage network for image deblurring via lightweight subnet learning

Multi-stage networks function by applying the concept of cascading, which alleviates the difficulties of network structure optimization using the single-stage method. Image deblurring methods based on multi-stage networks have previously been proposed and provided satisfactory results. However, owing to an excessive reliance on stacked subnets and residual blocks, most existing image deblurring methods that employ multi-stage networks suffer from two drawbacks: a complicated network structure and insufficient image representation of the model. To avoid these constraints, a novel multi-stage network deblurring model is proposed. Lightweight subnets are embedded in each stage of the model to gradually learn input image characteristics, which facilitates process optimization. The Ghost module is introduced as the basic unit of a neural network to reduce the required number of calculations and parameters. A wavelet reconstruction module is also added to avoid loss of image details. Finally, a more comprehensive loss function is designed to improve the quality of the generated images. Experimental results obtained using the GoPro dataset show that the proposed deblurring model achieves satisfactory performance in terms of both subjective results and objective evaluation.