Text-image super-resolution through anchored neighborhood regression with multiple class-specific dictionaries. Issue 2 (February 2017)

• Record Type: Journal Article
• Authors: Abedi, Ali; Kabir, Ehsanollah
• Is Part Of: Signal, image and video processing. Volume 11:Issue 2(2017)
• Page Start: 275
• Page End: 282
• Publication Date: 2017-02
• Subjects: Text-image super-resolution -- Class-specific dictionaries -- Anchored neighborhood regression -- Learning-based super-resolution; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0933-2
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract In the dictionary-based image super-resolution (SR) methods, the resolution of the input image is enhanced using a dictionary of low-resolution (LR) and high-resolution (HR) image patches. Typically, a single dictionary is learned from all the patches in the training set. Then, the input LR patch is super-resolved using its nearest LR patches and their corresponding HR patches in the dictionary. In this paper, we propose a text-image SR method using multiple class-specific dictionaries. Each dictionary is learned from the patches of images of a specific character in the training set. The input LR image is segmented into text lines and characters, and the characters are preliminarily classified. Likewise, overlapping patches are extracted from the input LR image. Then, each patch is super-resolved through the anchored neighborhood regression, usingn class-specific dictionaries corresponding to the top-n classification results of the character containing the patch. The final HR image is generated by aggregating all the super-resolved patches. Our method achieves significant improvements in visual image quality and OCR accuracy, compared to the related dictionary-based SR methods. This confirms the effectiveness of applying the preliminary character classification results and multiple class-specific dictionaries in text-image SR.