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

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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
