Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms / Edition 1

Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms / Edition 1

by Robert P. Loce, Edward R. Dougherty

ISBN-10: 081942109X

ISBN-13: 9780819421098

Pub. Date: 01/01/1997

Publisher: SPIE Press

Product Details

SPIE Press
Publication date:
Press Monographs
Product dimensions:
7.28(w) x 10.26(h) x 0.80(d)

Table of Contents

1Fundamentals of Digital Document Enhancement and Restoration
1.2The Problem of Spatial Resolution Conversion and Enhancement15
1.2.1Categories of resolution conversion20
1.3Enhancement by Quantization Range Conversion22
1.3.1The contour restoration problem23
1.3.2Methods of writing gray pixels25
1.3.3Noninferential image enhancement32
1.4Binary Image Filters32
1.5Basic Document Processing Operations42
2Resolution Conversion and Enhancement Technologies
2.1Inferential Methods51
2.2Noninferential Methods62
2.3Error Propagation Methods67
3Translation-Invariant Binary Operators
3.1Window Logic71
3.2Representation of Nonincreasing Operators75
3.3Representation of Increasing Operators77
3.4Basic Morphological Operators81
3.5General Filter Properties93
3.6Thinning and Thickening Filters94
3.7Differencing Filters96
4Optimal Mean-Absolute-Error Nonincreasing Binary Filters
4.1Conditional Expectation and Mean-Absolute Error102
4.2Optimal Nonincreasing Filters105
4.3Design of Optimal Nonincreasing Filters107
4.3.1Optimal parallel thinning and thickening110
4.3.2Optimal parallel differencing113
5Spatial Resolution Conversion and Enhancement Using Nonincreasing Operators
5.1The Problem of Spatial Resolution Conversion126
5.2Resolution Conversion by Multiple Parallel Filters127
5.2.1Integer conversion127
5.2.2Noninteger conversion138
5.3Resolution Conversion by Filtering in the Resampled Space143
6Quantization Range Conversion Using Nonincreasing Filters
6.1Design and Application of Gray-Scale Conditional Expectation Filters156
6.2Design and Application of Partial Coverage Filters160
7Optimal Mean-Absolute-Error Increasing Binary Filters
7.1Increasing Filters168
7.2Optimal Erosions170
7.3Optimal Increasing Filters172
7.4Design Constraints175
7.4.1Window constraint175
7.4.2Basis-size constraint175
7.4.3Library constraint176
7.5Error Representation178
7.6Error Relationship between Optimal Increasing and Nonincreasing Filters183
8Restoration by Increasing Binary Filters
8.1The Document Degradation and Restoration Setting189
8.2Restoration from Antiextensive Degradation191
8.2.1Restoration of thinned, broken characters191
8.2.2Restoration of characters with holes and breaks194
8.3Restoration from Extensive Degradations194
8.3.1Restoration of dilated characters with background noise194
8.4Filter Iteration196
8.4.1Restoration of ragged edges199
9Spatial Resolution Conversion Using Paired Increasing Operators
9.1Logic Cost Advantage of Increasing Operators for Spatial Resolution Conversion202
9.2Paired Filter Representation and Optimization203
10Application of Computational Morphology to Quantization Range Conversion
10.1Stack Filters211
10.2Computational Morphology216
10.3Statistical Estimation in the Computational Morphological Setting218
10.3.1Mean-absolute-error theorem for computational morphology219
10.4Image enhancement Using Computational Morphology221
10.5Iterative Design and Application of Computational Morphological Filters228
10.5.1Need for iterative methods228
10.5.2Representation of iterative computational morphological filters229
10.5.3Design of iterative computational morphological filters231

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