Self-supervised multi-scale neural network for blind deblurring

Blurry kernel estimation is a critical yet challenging task for blind deblurring. Most existing works devote to designing end-to-end networks that require a large amount of hard-to-obtain training data. In addition, these methods often ignore the intrinsic effects of blur kernel for blind deblurring. In this work, we present a unified latent image deblur and kernel estimation method based on MAP framework. By revisiting the coarse-to-fine strategy, we introduce a self-supervised multi-scale deblur network(MD-Net), where the multi-scale structure significantly reduce the kernel deviation caused by local area minimization. Specifically, our network commences with random inputs and outputs multi-scale reconstructed images and kernels. By progressively capturing the high-level configuration and low-level details from matching multi-resolution loss functions, the proposed MD-Net enable to capture multi-level image priors. Meanwhile, at each coarse level, we use Feature Extraction(FE) layers to further extract and emphasize features from reconstructed images. Compared with state-of-the-art blind deblurring methods, extensive experiments demonstrate that the proposed approach significantly improves the restoration performance in both quantitative and qualitative evaluations.