Rim Walha

Multiple Learned Dictionaries Based Clustered Sparse Coding for the Super-Resolution of Single Text Image

This paper addresses the problem of generating a super-resolved version of a low-resolution textual image by using Sparse Coding (SC) which suggests that image patches can be sparsely represented from a suitable dictionary. In order to enhance the learning performance and improve the reconstruction ability, we propose in this paper a multiple learned dictionaries based clustered SC approach for single text image super resolution. For instance, a large High-Resolution/Low-Resolution (HR/LR) patch pair database is collected from a set of high quality character images and then partitioned into several clusters by performing an intelligent clustering algorithm. Two coupled HR/LR dictionaries are learned from each cluster. Based on SC principle, local patch of a LR image is represented from each LR dictionary generating multiple sparse representations of the same patch. The representation that minimizes the reconstruction error is retained and applied to generate a local HR patch from the corresponding HR dictionary. The performance of the proposed approach is evaluated and compared visually and quantitatively to other existing methods applied to text images. In addition, experimental results on character recognition illustrate that the proposed method outperforms the other methods, involved in this study, by providing better recognition rates.

Super-resolution of single text image by sparse representation

This paper addresses the problem of generating a super-resolved text image from a single low-resolution image. The proposed Super-Resolution (SR) method is based on sparse coding which suggests that image patches can be well represented as a sparse linear combination of elements from a suitably chosen learned dictionary. Toward this strategy, a High-Resolution/Low-Resolution (HR/LR) patch pair data base is collected from high quality character images. To our knowledge, it is the first generic database allowing SR of text images may be contained in documents, signs, labels, bills, etc. This database is used to train jointly two dictionaries. The sparse representation of a LR image patch from the first dictionary can be applied to generate a HR image patch from the second dictionary. The performance of such approach is evaluated and compared visually and quantitatively to other existing SR methods applied to text images. In addition, we examine the influence of text image resolution on automatic recognition performance and we further justify the effectiveness of the proposed SR method compared to others.

Resolution enhancement of textual images via multiple coupled dictionaries and adaptive sparse representation selection

Resolution enhancement has become a valuable research topic due to the rapidly growing need for high-quality images in various applications. Various resolution enhancement approaches have been successfully applied on natural images. Nevertheless, their direct application to textual images is not efficient enough due to the specificities that distinguish these particular images from natural images. The use of insufficient resolution introduces substantial loss of details which can make a text unreadable by humans and unrecognizable by OCR systems. To address these issues, a sparse coding-based approach is proposed to enhance the resolution of a textual image. Three major contributions are presented in this paper: (1) Multiple coupled dictionaries are learned from a clustered database and selected adaptively for a better reconstruction. (2) An automatic process is developed to collect the training database, which contains writing patterns extracted from high-quality character images. (3) A new local feature descriptor well suited for writing specificities is proposed for the clustering of the training database. The performance of these propositions is evaluated qualitatively and quantitatively on various types of low-resolution textual images. Significant improvements in visual quality and character recognition rates are achieved using the proposed approach, confirmed by a detailed comparative study with state-of-the-art upscaling approaches.

Sparse Coding with a Coupled Dictionary Learning Approach for Textual Image Super-resolution

Sparse coding is widely known as a methodology where an input signal can be sparsely represented from a suitable dictionary. It was successfully applied on a wide range of applications like the textual image Super-Resolution. Nevertheless, its complexity limits enormously its application. Looking for a reduced computational complexity, a coupled dictionary learning approach is proposed to generate dual dictionaries representing coupled feature spaces. Under this approach, we optimize the training of a first dictionary for the high-resolution image space and then a second dictionary is simply deduced from the latter for the low-resolution image space. In contrast with the classical dictionary learning approaches, the proposed approach allows a noticeable speedup and a major simplification of the coupled dictionary learning phase both in terms of algorithm architecture and computational complexity. Furthermore, the resolution enhancement results achieved by applying the proposed approach on poorly resolved textual images lead to image quality improvements.

Single Textual Image Super-Resolution Using Multiple Learned Dictionaries Based Sparse Coding

In this paper, we propose a new approach based on sparse coding for single textual image Super-Resolution (SR). The proposed approach is able to build more representative dictionaries learned from a large training Low-Resolution/High-Resolution (LR/HR) patch pair database. In fact, an intelligent clustering is employed to partition such database into several clusters from which multiple coupled LR/HR dictionaries are constructed. Based on the assumption that patches of the same cluster live in the same subspace, we exploit for each local LR patch its similarity to clusters in order to adaptively select the appropriate learned dictionary over that such patch can be well sparsely represented. The obtained sparse representation is hence applied to generate a local HR patch from the corresponding HR dictionary. Experiments on textual images show that the proposed approach outperforms its counterparts in visual fidelity as well as in numerical measures.

Resolution enhancement of textual images: a survey of single image-based methods

Super-resolution (SR) task has become an important research area due to the rapidly growing interest for high quality images in various computer vision and pattern recognition applications. This has led to the emergence of various SR approaches. According to the number of input images, two kinds of approaches could be distinguished: single or multi-input based approaches. Certainly, processing multiple inputs could lead to an interesting output, but this is not the case mainly for textual image processing. This study focuses on single image-based approaches. Most of the existing methods have been successfully applied on natural images. Nevertheless, their direct application on textual images is not enough efficient due to the specificities that distinguish these particular images from natural images. Therefore, SR approaches especially suited for textual images are proposed in the literature. Previous overviews of SR methods have been concentrated on natural images application with no real application on the textual ones. Thus, this study aims to tackle this lack by surveying methods that are mainly designed for enhancing low-resolution textual images. The authors further criticise these methods and discuss areas which promise improvements in such task. To the best of the authors’ knowledge, this survey is the first investigation in the literature.

A Sparse Coding Based Approach for the Resolution Enhancement and Restoration of Printed and Handwritten Textual Images

Sparse coding has shown to be an effective technique in solving various reconstruction tasks such as denoising, in painting, and resolution enhancement of natural images. In this paper, we explore the use of this technique specifically to deal with low-resolution and degraded textual images. Firstly, we propose a sparse coding based resolution enhancement approach to recover a textual image with higher resolution than the input low-resolution one. It is based on the use of multiple coupled dictionaries which are learned from a clustered training low-resolution/high-resolution patch-pair database. A reconstruction scheme is then suggested in order to adaptively select the appropriate dictionaries that are useful for better recovering each local patch. This approach can be applied for the magnification of both printed and handwritten characters. Secondly, we propose to integrate the magnification in a restoration framework specifically to denoise and reconstruct at the same time degraded characters. The performances of these propositions are evaluated on various types of degraded printed and handwritten textual images where loss of details and background noise exist. Promising results are achieved when compared with results of other existing approaches.

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.

Handling noise in textual image resolution enhancement using online and offline learned dictionaries

The resolution enhancement of textual images poses a significant challenge mainly in the presence of noise. The inherent difficulties are twofold. First is the reconstruction of an upscaled version of the input low-resolution image without amplifying the effect of noise. Second is the achievement of an improved visual image quality and a better OCR accuracy. Classically, the issue is addressed by the application of a denoising step used as a preprocessing or a post-processing to the magnification process. Starting by a denoising process could be more promising to avoid any magnified artifacts while proceeding otherwise. However, the state of the art underlines the limitations of denoising approaches faced with the low spatial resolution of textual images. Recently, sparse coding has attracted increasing interest due to its effectiveness in different reconstruction tasks. This study proves that the application of an efficient sparse coding-based denoising process followed by the magnification process can achieve good restoration results even if the input image is highly noisy. The main specificities of the proposed sparse coding-based framework are: (1) cascading denoising and magnification of each image patch, (2) the use of sparsity stemmed from the non-local self-similarity given in textual images and (3) the use of dual dictionary learning involving both online and offline dictionaries that are selected adaptively for each local region of the input degraded image to recover its corresponding noise-free high-resolution version. Extensive experiments on synthetic and real low-resolution noisy textual images are carried out to validate visually and quantitatively the effectiveness of the proposed system. Promising results, in terms of image visual quality as well as character recognition rates, are achieved when compared it with the state-of-the-art approaches.

Deep Learning with Sparse Prior - Application to Text Detection in the Wild.

Text detection in the wild remains a very challenging task in computer vision. According to the state-of-theart, no text detector system, robust whatever the circumstances, exists up to date. For instance, the complexity and the diversity of degradations in natural scenes make traditional text detection methods very limited and inefficient. Recent studies reveal the performance of texture-based approaches especially including deep models. Indeed, the main strengthens of these models is the availability of a learning framework coupling feature extraction and classifier. Therefore, this study focuses on developing a new texture-based approach for text detection that takes advantage of deep learning models. In particular, we investigate sparse prior in the structure of PCANet; the convolution neural network known for its simplicity and rapidity and based on a cascaded principal component analysis (PCA). The added-value of the sparse coding is the representation of each feature map via coupled dictionaries to migrate from one level-resolution to an adequate lower-resolution. The specificity of the dictionary is the use of oriented patterns well-suited for textual pattern description. The experimental study performed on the standard benchmark, ICDAR 2003, proves that the proposed method achieves very promising results.