Scale-Iterative Upscaling Network for Image Deblurring

Abstract

Machine learning based methods for blind deblurring are efficient to handle real-world blurred images, whose blur may be caused by various combined distortions. However, existing multi-level architectures fail to fit images of various scenarios. In this paper, we propose a scale-iterative upscaling network (SIUN) that restores sharp images in an iterative manner. It is not only able to preserve the advantages of weights sharing across scales but also more flexible when training and predicting with different iterations to fit different images. Specifically, we bring in the super-resolution structure instead of the upsampling layer between two consecutive scales to restore a detailed image. Besides, we explore different curriculum learning strategies for both training and prediction of the network and introduce a widely applicable strategy to make SIUN compatible with different scenarios, including text and face. Experimental results on both benchmark datasets and real blurred images show that our method can produce better results than state-of-the-art methods. Code is available at https://github.com/minyuanye/SIUN.

Keywords

• Blind deblurring
• curriculum learning
• scale-iterative
• upscaling network