Abstract
In order to address the problem of the uncertainty of existing noise models and of the complexity and changeability of the edges and textures of low-resolution document images, this paper presents a projection onto convex sets (POCS) algorithm based on text features. The current method preserves the edge details and smooths the noise in text images by adding text features as constraints to original POCS algorithms and converting the fixed threshold to an adaptive one. In this paper, the optimized scale invariant feature transform (SIFT) algorithm was used for the registration of continuous frames, and finally the image was reconstructed under the improved POCS theoretical framework. Experimental results showed that the algorithm can significantly smooth the noise and eliminate noise caused by the shadows of the lines. The lines of the reconstructed text are smoother and the stroke contours of the reconstructed text are clearer, and this largely eliminates the text edge vibration to enhance the resolution of the document image text.
Highlights
In the early 1980s, Tsai and Huang were the first to use the Fourier-transform method for satellite image reconstruction [1]
Chen et al used the iterative gradient algorithm combined with the bilateral total variation algorithm to estimate motion for the subpixel level of target images based on the Taylor expansion, and they achieved good results [2]
A projection onto convex sets (POCS) algorithm is here proposed for super-resolution of text document images based on text features—which improves the original POCS algorithm over the degraded model and threshold—and optimizes the scale invariant feature transform (SIFT) algorithm at the registration stage to enhance the effect of document image reconstruction and ensure its efficiency
Summary
In the early 1980s, Tsai and Huang were the first to use the Fourier-transform method for satellite image reconstruction [1]. Panda et al used the iterative adaptive regularization method and genetic algorithm for image super-resolution [4]. On the basis of sparse representation, pointed out that, the shapes of the characters were not consistent, their edges and stroke curves were similar [6]. They found the ideal high-resolution (HR) image block by training the dictionary. The algorithms mentioned above have several disadvantages They are highly complex, require a single noise model, and cannot fully reconstruct the text edge information. A POCS algorithm is here proposed for super-resolution of text document images based on text features—which improves the original POCS algorithm over the degraded model and threshold—and optimizes the scale invariant feature transform (SIFT) algorithm at the registration stage to enhance the effect of document image reconstruction and ensure its efficiency
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