Digital Image Denoising in MATLAB

Inbunden, Engelska, 2024

1 464 kr

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Beskrivning

Presents a review of image denoising algorithms with practical MATLAB implementation guidance Digital Image Denoising in MATLAB provides a comprehensive treatment of digital image denoising, containing a variety of techniques with applications in high-quality photo enhancement as well as multi-dimensional signal processing problems such as array signal processing, radar signal estimation and detection, and more. Offering systematic guidance on image denoising in theories and in practice through MATLAB, this hands-on guide includes practical examples, chapter summaries, analytical and programming problems, computer simulations, and source codes for all algorithms discussed in the book. The book explains denoising algorithms including linear and nonlinear filtering, Wiener filtering, spatially adaptive and multi-channel processing, transform and wavelet domains processing, singular value decomposition, and various low variance optimization and low rank processing techniques. Throughout the text, the authors address the theory, analysis, and implementation of the denoising algorithms to help readers solve their image processing problems and develop their own solutions. Explains how the quality of an image can be quantified in MATLAB Discusses what constitutes a “naturally looking” image in subjective and analytical terms Presents denoising techniques for a wide range of digital image processing applications Describes the use of denoising as a pre-processing tool for various signal processing applications or big data analysis Requires only a fundamental knowledge of digital signal processing Includes access to a companion website with source codes, exercises, and additional resourcesDigital Image Denoising in MATLAB is an excellent textbook for undergraduate courses in digital image processing, recognition, and statistical signal processing, and a highly useful reference for researchers and engineers working with digital images, digital video, and other applications requiring denoising techniques.

Produktinformation

Utgivningsdatum:2024-07-11
Mått:152 x 229 x 18 mm
Vikt:454 g
Format:Inbunden
Språk:Engelska
Serie:IEEE Press
Antal sidor:224
Förlag:John Wiley & Sons Inc
ISBN:9781119617693

Utforska kategorier

Elektronik och kommunikationer inom Naturvetenskap och teknik

Mer om författaren

Chi-Wah Kok is the Founder and Executive Director of Canaan Semiconductor Pty Ltd, South Australia, Australia. He has worked with leading semiconductor companies, multimedia signal processing companies, research institutions, and universities including AT&T Labs Research, SONY Research Labs, Stanford University, and Hong Kong University of Science and Technology. Dr. Kok’s research interests include the application of signal processing methods in multimedia signal processing problems, solid state circuit design and modeling problems, and other real world problems. Wing-Shan Tam is the Founder and Engineering Manager of Canaan Semiconductor Pty Ltd, South Australia, Australia, where she works on advanced CMOS sensor design and high-power device structure and process development. Dr Tam’s research interests include mixed-signal integrated circuit design and image interpolation and color enhancement algorithm development.

Innehållsförteckning

Preface viAcknowledgments viiiAuthors xNomenclature xi1 Digital Image 11.1 Color Image 31.1.1 Color Filter Array and Demosaicing 51.1.2 Perceptual Color Space 51.1.3 Grayscale Image 71.2 Alternate Domain Image Representation 81.3 Digital Imaging in MATLAB 91.4 Current Pixel and Neighboring Pixels 101.4.1 Boundary Extension 111.5 Digital Image Noise 121.5.1 Random Noise 131.5.2 Gaussian Noise 141.5.3 Salt and Pepper Noise 181.6 Mixed Noise 191.7 Performance Evaluation 211.8 Image Quality Measure 221.8.1 Mean Squares Error 231.8.2 Peak Signal-to-Noise Ratio 251.8.3 Texture and Flat PSNR 261.8.4 Texture Area Classification 281.9 Structural Similarity 301.10 Brightness Normalization 331.11 Summary 331.12 Exercises 342 Filtering 362.1 Mean Filter 372.1.1 Gaussian Smoothing 422.2 Wiener Filter 442.3 Transform Thresholding 462.3.1 Overlapped Block 492.4 Median Filter 502.4.1 Noise Reduction Performance 522.4.2 Adaptive Median Filter 532.4.3 Median Filter with Predefined Mask 552.4.4 Median of Median 562.5 Summary 582.6 Exercises 583 Wavelet 603.1 2D Wavelet Transform 603.2 Noise Estimation 623.3 Wavelet Denoise 643.4 Thresholding 653.4.1 Threshold function 663.5 Threshold Value 683.5.1 Universal Threshold (Donoho Threshold) 683.6 Wavelet Wiener 753.7 Cycle Spinning 763.8 Fusion 803.8.1 Baseband Image Fusion 813.8.2 Detail Images Fusion 823.9 Which Wavelets to Use 853.10 Summary 863.11 Exercises 874 Rank Minimization 884.1 Singular Value Decomposition (SVD) 884.2 Threshold Denoising through AWGN Analysis 904.2.1 Noise Estimation 924.2.2 Denoising Performance 934.3 Blocked SVD 944.4 The Randomized Algorithm 974.4.1 Iterative Adjustment 984.5 Summary 1004.6 Exercises 1015 Variational Method 1035.1 Total Variation 1035.1.1 Rudin-Osher-Fatemi (ROF) Model 1045.1.2 Le-Chartrand-Asaki (LCA) Model 1045.1.3 Aubert-Aujol (AA) Model 1055.2 Gradient Descent ROF TV Algorithm 1055.2.1 Finite Difference Method 1065.3 Staircase Noise Artifacts 1095.4 Summary 1105.5 Exercises 1116 Nonlocal Means 1126.1 NonLocal Means 1126.1.1 Hard Threshold 1186.2 Adaptive Window Size 1206.2.1 Patch Window Size Adaptation 1216.2.2 Search Window Size Adaptation 1236.3 Summary 1256.4 Exercises 1267 Random Sampling 1277.1 Averaging Multiple Copies of Noisy Images 1287.2 Missing Pixels and Inpainting 1307.3 Singular Value Thresholding Inpainting 1317.4 Wavelet Image Fusion 1337.5 Summary 1357.6 Exercises 135Bibliography 141Index 142