Low quality document image modeling and enhancement

Moghaddam, Reza Farrahi; Cheriet, Mohamed

doi:10.1007/s10032-008-0076-2

Cited by 62 publications

(39 citation statements)

References 31 publications

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“…The ICA method is extended to doublesided documents in [18], using the recto and verso images as the sources for a blind-source separation. A model based approach is used by Moghaddam and Cheriet in [9], where a function of the difference in intensities between the two sides is used to indicate bleed-through regions. Physical diffusion-based models are defined for the foreground, bleed-through,and background, and then a reverse diffusion model is applied to bleed-through regions to remove interference.…”

Section: Previous Workmentioning

confidence: 99%

A Non-parametric Framework for Document Bleed-through Removal

Rowley-Brooke

Pitié

Kokaram

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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This paper presents recent work on a new framework for non-blind document bleed-through removal. The framework includes image preprocessing to remove local intensity variations, pixel region classification based on a segmentation of the joint recto-verso intensity histogram and connected component analysis on the subsequent image labelling. Finally restoration of the degraded regions is performed using exemplar-based image inpainting. The proposed method is evaluated visually and numerically on a freely available database of 25 scanned manuscript image pairs with ground truth, and is shown to outperform recent non-blind bleed-through removal techniques.

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Section: Previous Workmentioning

confidence: 99%

A Non-parametric Framework for Document Bleed-through Removal

Rowley-Brooke

Pitié

Kokaram

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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“…For example Sauvola's adaptive thresholding algorithm [12] followed by fuzzy classification is used in [2], the Kullback-Leibler (KL) thresholding algorithm and the binarisation algorithm of Gatos et al [6] are extended by adding in second threshold levels for the bleed-through interference in [1], and ICA is extended to double-sided documents in [16], using the recto and the flipped verso images as the sources. A model based approach is used in [9], where a function of the difference in intensities between the two sides is used to locate bleed-through regions. Physical diffusion-based models are defined for the foreground text, bleed-through interference, and the background medium, and then a reverse diffusion model is applied on bleed-through regions to remove interference.…”

Section: Bleed-through Removalmentioning

confidence: 99%

“…Secondly Moghaddam and Cheriet incorporated the diffusion model idea in [9] to a unified framework in [10], (referred to as Mogh), using variational models for non-blind and blind bleed-through removal. Their double-sided wavelet method uses, again, a function of the difference in intensity between the degraded recto and verso sides as an indicator of bleed-through and foreground text regions, and spatial smoothness is enforced in the wavelet domain.…”

Section: Bleed-through Removalmentioning

confidence: 99%

A Ground Truth Bleed-Through Document Image Database

Rowley-Brooke

Pitié

Kokaram

2012

Lecture Notes in Computer Science

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“…As per the non-blind methods, a model based approach using differences in the intensities of recto and verso side is outlined in [12]. The same model is extended in [13] using variational models with spatial smoothness in the wavelet domain.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Bleed-through is a frequent, pervasive degradation in ancient manuscripts, which is caused by ink seeped from the opposite side of the sheet. Bleed-through, appearing as an extra interfering text, hinders document readability and makes it difficult to decipher the information contents. Digital image restoration techniques have been successfully employed to remove or significantly reduce this distortion. This paper proposes a two-step restoration method for documents affected by bleed-through, exploiting information from the recto and verso images. First, the bleed-through pixels are identified, based on a non-stationary, linear model of the two texts overlapped in the recto-verso pair. In the second step, a dictionary learning-based sparse image inpainting technique, with non-local patch grouping, is used to reconstruct the bleed-through-contaminated image information. An overcomplete sparse dictionary is learned from the bleed-through-free image patches, which is then used to estimate a befitting fill-in for the identified bleed-through pixels. The non-local patch similarity is employed in the sparse reconstruction of each patch, to enforce the local similarity. Thanks to the intrinsic image sparsity and non-local patch similarity, the natural texture of the background is well reproduced in the bleed-through areas, and even a possible overestimation of the bleed through pixels is effectively corrected, so that the original appearance of the document is preserved. We evaluate the performance of the proposed method on the images of a popular database of ancient documents, and the results validate the performance of the proposed method compared to the state of the art.

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Low quality document image modeling and enhancement

Cited by 62 publications

References 31 publications

A Non-parametric Framework for Document Bleed-through Removal

A Non-parametric Framework for Document Bleed-through Removal

A Ground Truth Bleed-Through Document Image Database

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

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