Joint denoising and magnification of noisy Low-Resolution textual images

Current issues on textual image magnification have been focused on noise-free low-resolution images. Nevertheless, real circumstances are far from these assumptions and existing systems are generally confronted with noisy images; limiting thus the efficiency of the magnification process. The scope of this study is to propose a joint denoising and magnification system based on sparse coding to tackle such a problem. The underlying idea suggests the representation of an image patch by a linear combination of few elements from a suitable dictionary. The proposed system uses both online and offline learned dictionaries that are selected adaptively for each image patch of the input Low-Resolution (LR) noisy image to generate its corresponding noise-free High-Resolution (HR) version. In fact, the online learned dictionaries are trained on a clustered dataset of the image patches selected from the input image and used for the denoising purpose in order to take benefit of the non-local self-similarity assumption in textual images. For the offline learned dictionaries, they are trained on an external LR/HR image patch pair dataset and employed for the magnification purpose. The performance of the proposed system is evaluated visually and quantitatively on different LR noisy textual images and promising results are achieved when compared with other existing systems and conventional approaches dealing with such kind of images.