Deblurring of Document Images Based on Sparse Representations Enhanced by Non-local Means
Nibal Nayef, Petra Gomez‐Krämer, Jean-Marc Ogier · 2014
Blur is one of the most difficult distortions in camera captured documents. It degrades the visual quality of an image, and makes it difficult to read whether by a human or OCR systems. This paper presents a novel non-blind deblurring method that combines the well known effective techniques of sparse representations and non-local image similarity. The presented problem formulation enables the use of standard sparse coding methods for solving sparse coding-based deblurring when enhanced by a non-local means prior. The method has been tested on both synthetic and real document images degraded with a variety of blur kernels. The resulting deblurred images have high quality in terms of both signal-to-noise ratio and OCR accuracy.