LEADER 06395nam 22005293 450 001 9911019324003321 005 20240607080433.0 010 $a9781119617778 010 $a1119617774 010 $a9781119617754 010 $a1119617758 035 $a(MiAaPQ)EBC31368819 035 $a(Au-PeEL)EBL31368819 035 $a(CKB)32238909000041 035 $a(Perlego)4442241 035 $a(OCoLC)1438670922 035 $a(EXLCZ)9932238909000041 100 $a20240607d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDigital Image Denoising in MATLAB 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2024. 210 4$dİ2024. 215 $a1 online resource (227 pages) 225 1 $aIEEE Press Series 311 08$a9781119617693 311 08$a1119617693 327 $aCover -- Title Page -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgments -- Nomenclature -- About the Companion Website -- Chapter 1 Digital Image -- 1.1 Color Image -- 1.1.1 Color Filter Array and Demosaicing -- 1.1.2 Perceptual Color Space -- 1.1.3 Grayscale Image -- 1.2 Alternate Domain Image Representation -- 1.3 Digital Imaging in MATLAB -- 1.4 Current Pixel and Neighboring Pixels -- 1.4.1 Boundary Extension -- 1.5 Digital Image Noise -- 1.5.1 Random Noise -- 1.5.2 Gaussian Noise -- 1.5.2.1 Noise Power Estimation -- 1.5.2.2 Noise Power Estimation Base on Derivative -- 1.5.3 Salt and Pepper Noise -- 1.6 Mixed Noise -- 1.7 Performance Evaluation -- 1.8 Image Quality Measure -- 1.8.1 Mean Squares Error -- 1.8.2 Peak Signal?to?Noise Ratio -- 1.8.3 Texture and Flat PSNR -- 1.8.4 Texture Area Classification -- 1.9 Structural Similarity -- 1.10 Brightness Normalization -- 1.11 Summary -- Exercises -- Chapter 2 Filtering -- 2.1 Mean Filter -- 2.1.1 Gaussian Smoothing -- 2.2 Wiener Filter -- 2.3 Transform Thresholding -- 2.3.1 Overlapped Block -- 2.4 Median Filter -- 2.4.1 Noise Reduction Performance -- 2.4.2 Adaptive Median Filter -- 2.4.3 Median Filter with Predefined Mask -- 2.4.4 Median of Median -- 2.5 Summary -- Exercises -- Chapter 3 Wavelet -- 3.1 2D Wavelet Transform -- 3.2 Noise Estimation -- 3.3 Wavelet Denoise -- 3.4 Thresholding -- 3.4.1 Threshold Function -- 3.5 Threshold Value -- 3.5.1 Universal Threshold (Donoho Threshold) -- 3.5.1.1 Adaptive Threshold -- 3.6 Wavelet Wiener -- 3.7 Cycle Spinning -- 3.8 Fusion -- 3.8.1 Baseband Image Fusion -- 3.8.1.1 Simple Average -- 3.8.1.2 Arithmetic Combination -- 3.8.1.3 Correlation Base -- 3.8.2 Detail Images Fusion -- 3.8.2.1 Simple Average -- 3.8.2.2 Select Max -- 3.8.2.3 Cross Band Fusion -- 3.9 Which Wavelets to Use -- 3.10 Summary -- Exercises. 327 $aChapter 4 Rank Minimization -- 4.1 Singular Value Decomposition (SVD) -- 4.2 Threshold Denoising Through AWGN Analysis -- 4.2.1 Noise Estimation -- 4.2.2 Denoising Performance -- 4.3 Blocked SVD -- 4.4 The Randomized Algorithm -- 4.4.1 Iterative Adjustment -- 4.5 Summary -- Exercises -- Chapter 5 Variational Method -- 5.1 Total Variation -- 5.1.1 Rudin-Osher-Fatemi (ROF) Model -- 5.1.2 Le-Chartrand-Asaki (LCA) Model -- 5.1.3 Aubert-Aujol (AA) Model -- 5.2 Gradient Descent ROF TV Algorithm -- 5.2.1 Finite Difference Method -- 5.3 Staircase Noise Artifacts -- 5.4 Summary -- Exercises -- Chapter 6 NonLocal Means -- 6.1 NonLocal Means -- 6.1.1 Hard Threshold -- 6.2 Adaptive Window Size -- 6.2.1 Patch Window Size Adaptation -- 6.2.2 Search Window Size Adaptation -- 6.3 Summary -- Exercises -- Chapter 7 Random Sampling -- 7.1 Averaging Multiple Copies of Noisy Images -- 7.2 Missing Pixels and Inpainting -- 7.3 Singular Value Thresholding Inpainting -- 7.4 Wavelet Image Fusion -- 7.5 Summary -- Exercises -- Appendix A MATLAB Functions List -- References -- Index -- EULA. 330 $a"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 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 resources Digital 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"--$cProvided by publisher. 410 0$aIEEE Press Series 606 $aImage processing$xDigital techniques 615 0$aImage processing$xDigital techniques. 676 $a006.6 700 $aKok$b Chi-Wah$01638178 701 $aTam$b Wing-Shan$01667930 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019324003321 996 $aDigital Image Denoising in MATLAB$94416160 997 $aUNINA LEADER 04322nam 2201153z- 450 001 9910619467903321 005 20221025 010 $a3-0365-5060-7 035 $a(CKB)5670000000391595 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/93181 035 $a(oapen)doab93181 035 $a(EXLCZ)995670000000391595 100 $a20202210d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aGluten-Related Disorders: Time to Move from Gut to Brain 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (158 p.) 311 08$a3-0365-5059-3 330 $aThe extraintestinal manifestations of coeliac disease (CD) are now well recognised. 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