Beyond pixels and regions: A non local patch means (NLPM) method for content-level restoration, enhancement, and reconstruction of degraded document images

TL;DR

This paper introduces a non-local patch means method for content-aware restoration and enhancement of degraded document images, leveraging patch-based content descriptors and a genetic algorithm for efficient, high-level image reconstruction.

Contribution

It presents a novel patch-based, content-level restoration approach that incorporates high-level information and uses a genetic algorithm for efficient patch matching.

Findings

Effective restoration of degraded document images

Promising results on DIBCO'09 dataset

Low computational load due to genetic algorithm

Abstract

A patch-based non-local restoration and reconstruction method for preprocessing degraded document images is introduced. The method collects relative data from the whole input image, while the image data are first represented by a content-level descriptor based on patches. This patch-equivalent representation of the input image is then corrected based on similar patches identified using a modified genetic algorithm (GA) resulting in a low computational load. The corrected patch-equivalent is then converted to the output restored image. The fact that the method uses the patches at the content level allows it to incorporate high-level restoration in an objective and self-sufficient way. The method has been applied to several degraded document images, including the DIBCO'09 contest dataset with promising results.