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

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1997 Hard cover New book w/ slightly shelfworn to cover. -ships immediately Sewn binding. Paper over boards. 260 p. Audience: General/trade.

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Describes and defines the practice as used in modern offices and print shops, including the fundamental image processing problems and current industry practices; and explores the design of statistically optimal algorithms for the task, using simple idealized examples of both algorithms and document processing throughout. Resolution conversion, translation-invariant binary operators, and quantization range conversion with non-increasing filters are among the topics. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

  • ISBN-13: 9780819421098
  • Publisher: SPIE Press
  • Publication date: 1/1/1997
  • Series: Press Monographs
  • Edition number: 1
  • Pages: 260
  • Product dimensions: 7.28 (w) x 10.26 (h) x 0.80 (d)

Table of Contents

Preface ix
1 Fundamentals of Digital Document Enhancement and Restoration
1.1 Definitions 1
1.2 The Problem of Spatial Resolution Conversion and Enhancement 15
1.2.1 Categories of resolution conversion 20
1.3 Enhancement by Quantization Range Conversion 22
1.3.1 The contour restoration problem 23
1.3.2 Methods of writing gray pixels 25
1.3.3 Noninferential image enhancement 32
1.4 Binary Image Filters 32
1.5 Basic Document Processing Operations 42
2 Resolution Conversion and Enhancement Technologies
2.1 Inferential Methods 51
2.2 Noninferential Methods 62
2.3 Error Propagation Methods 67
3 Translation-Invariant Binary Operators
3.1 Window Logic 71
3.2 Representation of Nonincreasing Operators 75
3.3 Representation of Increasing Operators 77
3.4 Basic Morphological Operators 81
3.5 General Filter Properties 93
3.6 Thinning and Thickening Filters 94
3.7 Differencing Filters 96
4 Optimal Mean-Absolute-Error Nonincreasing Binary Filters
4.1 Conditional Expectation and Mean-Absolute Error 102
4.2 Optimal Nonincreasing Filters 105
4.3 Design of Optimal Nonincreasing Filters 107
4.3.1 Optimal parallel thinning and thickening 110
4.3.2 Optimal parallel differencing 113
5 Spatial Resolution Conversion and Enhancement Using Nonincreasing Operators
5.1 The Problem of Spatial Resolution Conversion 126
5.2 Resolution Conversion by Multiple Parallel Filters 127
5.2.1 Integer conversion 127
5.2.2 Noninteger conversion 138
5.3 Resolution Conversion by Filtering in the Resampled Space 143
6 Quantization Range Conversion Using Nonincreasing Filters
6.1 Design and Application of Gray-Scale Conditional Expectation Filters 156
6.2 Design and Application of Partial Coverage Filters 160
7 Optimal Mean-Absolute-Error Increasing Binary Filters
7.1 Increasing Filters 168
7.2 Optimal Erosions 170
7.3 Optimal Increasing Filters 172
7.4 Design Constraints 175
7.4.1 Window constraint 175
7.4.2 Basis-size constraint 175
7.4.3 Library constraint 176
7.5 Error Representation 178
7.6 Error Relationship between Optimal Increasing and Nonincreasing Filters 183
8 Restoration by Increasing Binary Filters
8.1 The Document Degradation and Restoration Setting 189
8.2 Restoration from Antiextensive Degradation 191
8.2.1 Restoration of thinned, broken characters 191
8.2.2 Restoration of characters with holes and breaks 194
8.3 Restoration from Extensive Degradations 194
8.3.1 Restoration of dilated characters with background noise 194
8.4 Filter Iteration 196
8.4.1 Restoration of ragged edges 199
9 Spatial Resolution Conversion Using Paired Increasing Operators
9.1 Logic Cost Advantage of Increasing Operators for Spatial Resolution Conversion 202
9.2 Paired Filter Representation and Optimization 203
10 Application of Computational Morphology to Quantization Range Conversion
10.1 Stack Filters 211
10.2 Computational Morphology 216
10.3 Statistical Estimation in the Computational Morphological Setting 218
10.3.1 Mean-absolute-error theorem for computational morphology 219
10.4 Image enhancement Using Computational Morphology 221
10.5 Iterative Design and Application of Computational Morphological Filters 228
10.5.1 Need for iterative methods 228
10.5.2 Representation of iterative computational morphological filters 229
10.5.3 Design of iterative computational morphological filters 231
References 236
Index 250
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