Non-Local Sparse Image Inpainting for Document Bleed-Through Removal

Hanif, Muhammad Imran; Tonazzini, Anna; Savino, Pasquale; Salerno, Emanuele

doi:10.3390/jimaging4050068

Cited by 15 publications

(2 citation statements)

References 62 publications

(81 reference statements)

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“…Also, in [ 10 ], where a dual-layer Markov Random Field (MRF) prior is combined with a data term derived from user-labeled pixels, and in [ 11 ], where correlated component analysis is used to separate the information layers. In [ 12 ], the fidelity of the restored manuscript to the original one is further improved by inpainting the bleed-through pixels, detected by the model-based method in [ 13 ], through sparse image representation and dictionary learning.…”

Section: Introductionmentioning

confidence: 99%

Restoration and content analysis of ancient manuscripts via color space based segmentation

et al. 2023

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Ancient manuscripts are a rich source of history and civilization. Unfortunately, these documents are often affected by different age and storage related degradation which impinge on their readability and information contents. In this paper, we propose a document restoration method that removes the unwanted interfering degradation patterns from color ancient manuscripts. We exploit different color spaces to highlight the spectral differences in various layers of information usually present in these documents. At each image pixel, the spectral representations of all color spaces are stacked to form a feature vector. PCA is applied to the whole data cube to eliminate correlation of the color planes and enhance separation among the patterns. The reduced data cube, along with the pixel spatial information, is used to perform a pixel based segmentation, where each cluster represents a class of pixels that share similar color properties in the decorrelated color spaces. The interfering, unwanted classes can thus be removed by inpainting their pixels with the background texture. Assuming Gaussian distributions for the various classes, a Gaussian Mixture Model (GMM) is estimated through the Expectation Maximization (EM) algorithm from the data, and then used to find appropriate labels for each pixel. In order to preserve the original appearance of the document and reproduce the background texture, the detected degraded pixels are replaced based on Gaussian conditional simulation, according to the surrounding context. Experiments are shown on manuscripts affected by different kinds of degradations, including manuscripts from the DIBCO 2018 and 2019 publicaly available dataset. We observe that the use of a few PCA dominant components accelerates the clustering process and provides a more accurate segmentation.

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Section: Introductionmentioning

confidence: 99%

Restoration and content analysis of ancient manuscripts via color space based segmentation

et al. 2023

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“…The first three articles deal with historical document preprocessing. The work by Hanif et al [1] aims at removing bleed-through using a non-linear model, and at reconstructing the background by an inpainting approach based on non-local patch similarity. The paper by Almeida et al [2] proposes a new binarization approach that includes a decision-based process for finding the best threshold for each RGB channel.…”

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confidence: 99%