Removing salt-and-pepper noise in text/graphics images

Chinnasarn, Krisana; Rangsanseri, Yuttapong; Thitimajshima, Punya

doi:10.1109/apccas.1998.743809

Cited by 30 publications

(20 citation statements)

References 2 publications

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“…The procedure studies the structure of thin lines before deciding to remove or retain the thin lines. An algorithm that incorporates TAMD is presented in this article, and it is based on a single iteration filter named Enhanced kFill [4]. The proposed algorithm can eliminate salt-and-pepper noise and one-pixel-wide noise, while preserving thin graphical elements.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Salt-and-Pepper Noise Removal in Binary Images of Engineering Drawing

Al-Khaffaf

Talib

Salam

2009

IEICE Trans. Inf. & Syst.

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SUMMARYNoise removal in engineering drawing is an important operation performed before other image analysis tasks. Many algorithms have been developed to remove salt-and-pepper noise from document images. Cleaning algorithms should remove noise while keeping the real part of the image unchanged. Some algorithms have disadvantages in cleaning operation that leads to removing of weak features such as short thin lines. Others leave the image with hairy noise attached to image objects. In this article a noise removal procedure called TrackAndMayDel (TAMD) is developed to enhance the noise removal of salt-and-pepper noise in binary images of engineering drawings. The procedure could be integrated with third party algorithms' logic to enhance their ability to remove noise by investigating the structure of pixels that are part of weak features. It can be integrated with other algorithms as a post-processing step to remove noise remaining in the image such as hairy noise attached with graphical elements. An algorithm is proposed by incorporating TAMD in a third party algorithm. Real scanned images from GREC'03 contest are used in the experiment. The images are corrupted by salt-and-pepper noise at 10%, 15%, and 20% levels. An objective performance measure that correlates with human vision as well as MSE and PSNR are used in this experiment. Performance evaluation of the introduced algorithm shows better-quality images compared to other algorithms. key words: salt/pepper noise removal, impulsive noise, engineering drawing, binary image, document analysis and recognition

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Section: Introductionmentioning

confidence: 99%

Enhancing Salt-and-Pepper Noise Removal in Binary Images of Engineering Drawing

Al-Khaffaf

Talib

Salam

2009

IEICE Trans. Inf. & Syst.

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“…Closing these openings and applying clutter removal (as in Section 2.3) on the resultant clutter-component will remove this noise along with clutter. Though these openings resemble salt-noise, known methods for salt-noise removal like median filtering [3] can not be used, as these openings are extremely dense (unlike SnP noise) or big enough to escape removal in a prefixed size mask. Also, closing using a smaller structuring element may not close all these openings while using a bigger element may close the text-loops as in 'g','o' etc.…”

Section: Complete Removal Of Clutter With Blurring Boundariesmentioning

confidence: 99%

“…1 The partial support of this research by DARPA through BBN/DARPA Award HR001108C0004 and the US Government through NSF Award 1150713501 is gratefully acknowledged Ink blobs, salt-n-pepper [3], stray marks, marginal noise [4] are, in general, independent of location, size or other properties of text data in the document image. Recorded images having this type of noise, can be expressed as the sum of true image I(i, j) and the noise N (i, j) as R(i, j) = I(i, j) + N (i, j) Blur, pixel-shift or bleed-through [12] on other hand, manifest themselves differently depending on the content.…”

Section: Introductionmentioning

confidence: 99%

“…Unwanted punched holes and stray marks exhibit regularity in their shapes while ruled lines [13,14] show periodicity in their positions as well. On the other hand, noise such has ink blobs, complex background binarized patterns, marginal noise [4] and salt-n-pepper [3,1] often lack any consistent property. This 'irregular noise' has typically been classified with simple rule based features.…”

Section: Introductionmentioning

confidence: 99%

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Clutter noise removal in binary document images

Agrawal

Doermann

2013

IJDAR

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The paper presents a clutter detection and removal algorithm for complex document images. The distance transform based approach is independent of clutter's position, size, shape and connectivity with text. Features are based on a residual image obtained by analysis of the distance transform and clutter elements, if present, are identified with an SVM classifier. Removal is restrictive, so text attached to the clutter is not deleted in the process. The method was tested on a collection of degraded and noisy, machine-printed and handwritten Arabic and English text documents. Results show pixel-level accuracies of 97.5% and 95% for clutter detection and removal respectively.This approach was also extended with a noise detection and removal model for documents having a mix of clutter and salt-n-pepper noise.

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“…The three factors were noise-removal method, noise level, and raster-to-vector method. The study was performed using six noise-removal methods: kFill [17], [18], Enhanced kFill (EkFill) [19], Activity Detector (AD) [20], and their respective enhanced counterparts Algorithm A (AlgA) [21], Algorithm B (AlgB) [22], and Algorithm C (AlgC) [23]. Three noise levels were studied: 5%, 10% and 15%.…”

Section: Copyright C 2011 the Institute Of Electronics Information Amentioning

confidence: 99%

Empirical Performance Evaluation of Raster-to-Vector Conversion Methods: A Study on Multi-Level Interactions between Different Factors

Al-Khaffaf

Talib

Salam

2011

IEICE Trans. Inf. & Syst.

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SUMMARYMany factors, such as noise level in the original image and the noise-removal methods that clean the image prior to performing a vectorization, may play an important role in affecting the line detection of raster-to-vector conversion methods. In this paper, we propose an empirical performance evaluation methodology that is coupled with a robust statistical analysis method to study many factors that may affect the quality of line detection. Three factors are studied: noise level, noise-removal method, and the raster-to-vector conversion method. Eleven mechanical engineering drawings, three salt-and-pepper noise levels, six noise-removal methods, and three commercial vectorization methods were used in the experiment. The Vector Recovery Index (VRI) of the detected vectors was the criterion used for the quality of line detection. A repeated measure ANOVA analyzed the VRI scores. The statistical analysis shows that all the studied factors affected the quality of line detection. It also shows that two-way interactions between the studied factors affected line detection.

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Removing salt-and-pepper noise in text/graphics images

Cited by 30 publications

References 2 publications

Enhancing Salt-and-Pepper Noise Removal in Binary Images of Engineering Drawing

Enhancing Salt-and-Pepper Noise Removal in Binary Images of Engineering Drawing

Clutter noise removal in binary document images

Empirical Performance Evaluation of Raster-to-Vector Conversion Methods: A Study on Multi-Level Interactions between Different Factors

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