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In this paper, we propose a novel end-to-end model for document deblurring using cycle-consistent adversarial networks. The main objective of this work is to achieve image deblurring without knowledge of the blur kernel. Our method, named 'Blur2Sharp CycleGAN', generates a sharp image from a blurry one and shows how CycleGAN can be used in document deblurring. Using only a blurred image as input, we try to generate the sharp image. Thus, no information about the blur kernel is required. In the evaluation part, we use Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to compare the deblurring images. The experiments demonstrate a clear improvement in visual quality with respect to the state-of-the-art using a dataset of text images.

We suggest a novel approach that performs jointlysuper-resolution and deblurring from a low blurry image. Theexperimental results have achieved state-of-the-art performancein PSNR and SSIM metrics. Thus, we confirm that DCSCNprovides satisfactory results for enhancement tasks on low blurryimages.

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Model for document deblurring using cycle-consistent adversarial networks