A POCS Algorithm Based on Text Features for the Reconstruction of Document Images at Super-Resolution

Abstract

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.

Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images

“…Xi [ 19 ] improved the initial image estimation by using the wavelet bicubic interpolation for POCS reconstruction algorithm; the experimental results are evident. Liang [ 20 ] presented a POCS algorithm based on text features; in this method, text features are added as constraints to preserve the edge details and smoothen the noise in the text images. Meanwhile, the blind SR method is proposed by Sroubek and Flusser [ 21 ] can incorporates blur estimation into SR by performing an advanced deconvolution task.…”