manipulation can be **used** effectively for **image** enhancement Histograms can be **used** to provide **useful** **image** statistics Information derived from histograms are quite **useful** in other **image** processing applications, such as **image** **compression** and segmentation. Histogram of the **image**: histogram Ch3, / color,r,linestyle,--,Marker,s) the arguments are specified preciously. Histogram equalization of the **image**: We have this **image** in **matlab** called pout.tif, when we plot its histogram it is showed like this: Notice /

**Image** Hybrid **Image** in Laplacian Pyramid High frequency Low frequency In **matlab** on our data, and a brief project highlight. **Image** representation Pixels: great for spatial resolution, poor access to frequency Fourier transform: great for frequency, not for spatial info Pyramids/filter banks: balance between spatial and frequency information Major **uses** of **image** pyramids **Compression**/ low frequencies, the bottom right – high frequencies **Image** **compression** **using** DCT Quantize – More coarsely for high frequencies (/

So, what is this class? **Image** and Video Coding and Processing will cover: –Multidimensional sampling and filtering –Models for the Human Visual System –Color Modeling and Representation of **Images** –Denoising –Pattern recognition –**Image** and Video **Compression** –Watermarking Course Resources Required Textbook:/IEEE or ACM –Most likely, I will make the papers available on the course website Other **useful** resource: **MATLAB**’s **Image** Processing Toolbox –Goto www.mathworks.com The Dirty Work… This class is not a “cake-/

of toolbox(build-in functions) for **use** Strongly suggested that you can **use** **Matlab** for your homework Command-line interpreter C-like grammar Introduction **Image** processing system Elements of an digital **image** processing system **Image** acquisition Storage Processing Communication Display **Image** acquisition analog **image** sampling 取樣 digital **image** **Image** storage Pixel ( 像素 ) Position Intensity Binary **image**: 0,1 Grayscale **image**: 0-255(8 bits) Color **image**: R, G, B **Compressed** **image** format.jpg,.gif, …, 節省儲存空間 Raw data: no/

generated by different **imaging** systems; – comparison of **images** obtained **using** different **imaging** parameter settings of a given system; – comparison of the results of **image** enhancement algorithms; – assessment of the effect of the passage of an **image** through a transmission channel or medium; and – assessment of **images** **compressed** by different data **compression** techniques at different rates of loss. Characterization of **Image** Quality The **image** quality and information content of biomedical **images** **image** depends on/

perform on the sub matrix: Decoding of the **compressed** **image** **using** Huffman and Differential decoding De-Zig Zag operation De quantization Inverse DCT and Adding 128 to the **image** bit map The module sends back the sub /RGB731- דצמ 07- ינו 3 Simulate implementation(VHDL) + time assessment-bottle neck + hardware improvements5407- ינו 02- מרץ 4Skipping the **use** of **Matlab** aux scripts723- פבר 02- מרץ Gantt chart 2.3.2014 Final A presentation Resources http://www.cs.northwestern.edu/~agupta/_projects/image_processing//

. F5 was implemented without a random walk generator. (no need for password) Inputs is gray **image** matrix. Output is DCT coefficients matrix. JPEG **compression**/decompression process is not included. Only the F5 embedding and extraction process is implemented. We implement F5 algorithm **using** **matlab** 7. Only the core embedding and extraction process is implemented. We implement F5 without random walk generator. Accept only grayscale/

! Audio, **image** and video **compression** New JPEG standard includes wavelet **compression** FBI’s fingerprints database saved as wavelet-**compressed** Signal denoising, interpolation, **image** zooming, texture analysis, time-scale feature extraction In our context, WT will be **used** primarily as a feature extraction tool Remember, WT is just a change of basis, in order to extract **useful** information which might otherwise not be easily seen WT in **MATLAB** **MATLAB** has/

can be thresholded, it can be **compressed** Thresholding removes excessively small features and replaces with a zero (much easier to **compress** than a 64b double) Wavelet transforms are well suited for this purpose and have been **used** in **image** **compression** (jpeg 2000) Why Wavelets? / the process I could try my hand at some parallel processing I could have a native 3-D transformation And **Matlab** makes my computer very slow Demo./wavecomp –c 1 –d 2 vorticity000.dat./wavedec vorticity000.dat.wcm Algorithm The /

**compression**) Writing **Images** In order to get an idea of the **compression** achieved and to obtain another **image** file details, we can **use** function/**MATLAB**. - The frequently **used** data classes that encountered in **image** processing are double, uint8 and logical. - Logical arrays are created by **using** function logical or by **using** relational operators **Image** Types The toolbox supports four types of **images**: –Intensity **Images** –Binary **Images** –Indexed **Images** –RGB **Images** Intensity **Images** (Grayscale **Images**) An intensity **image**/

Black / White Red / Green Blue / Yellow PS221 project : pattern sensitivity and **image** **compression** Eric Setton - Winter 2002 GUI **used** for the experiments This is what the **Matlab** Gui looks like : PS221 project : pattern sensitivity and **image** **compression** Eric Setton - Winter 2002 Comparing the color spaces The differences in the subsampled **image** reflect the efficiency of the space for **compression** The color space YCbCr performs best and is more/

to match optimized implementation Pulse **Compression** Output (**MatLab**) 671 samples out of PC Benchmark Pulse **Compression** Input/Output (Actual) Pulse **Compression** Reference (Actual)* Benchmark Benchmark Measurements: Validate Pulse **Compression** performance with hardware and with /% Optimized Pulse **Compression** functions modified **using** COTS SDK and integrated onto Host platform VSIPL Core Lite Libraries under development **Image** Processing Signal Processing **Compression**/De-**compression** Wide Ranging Applicability/

. Writing **Images** In order to get an idea of the **compression** achieved and to obtain another **image** file details, we can **use** function imfinfo/**MATLAB**. Data Classes - The frequently **used** data classes that encountered in **image** processing are double, uint8 and logical. - Logical arrays are created by **using** function logical or by **using** relational operators **Image** Types The toolbox supports four types of **images**: –Intensity **Images** –Binary **Images** –RGB **Images** Intensity **Images** (Grayscale **Images**) An intensity **image**/

The **MATLAB** script rect2spr gives a simple conversion of an **image** from rectangular architecture to spiral architecture References Xiangjian He, Wenjing Jia, Qiang Wu, Namho Hur, Tom Hintz, Huaqing Wang and Jinwoong Kim, ” Basic Transformations on Virtual Hexagonal Structure”, Proceedings of the international conference on Computer Graphics, **Imaging** and Visualization, 2006. Wang, H., Wang, M., Hintz, T., He, X. and Wu,” Fractal **Image** **Compression** on/

DCT Progressive DCT Lossless Hierarchical **Image** Processing Architecture, © 2001-2004 Oleh TretiakPage 15Lecture 7 JPEG in practice Lossy DCT coding **used** most often Some **use** of sequential DCT (Web **images**) Lossless? Hierarchical? JPEG DCT allows many varieties/ **compression** **Image** Processing Architecture, © 2001-2004 Oleh TretiakPage 38Lecture 7 Example of JPEG **compression** Very high quality: **compression** = 2.33 Photoshop **Image** Very low quality: **compression** = 115 Produced by **MATLAB** with Quality = 0 **Image**/

: This is a linear programming problem and can be solved with any LP solver (in **Matlab**, for example) or with packages like L1-magic. Signal Reconstruction The first **use** of L1-norm for signal reconstruction goes back to a PhD thesis in 1965 – by /varied from 1000 to 30000. Note that this is equivalent to **using** a sensing matrix F of size m by N. **Compressive** Sensing: Toy Example with **images** In the second method, we reconstruct the **image** **using** the first 1000 largest DCT coefficients and a random selection of /

**MATLAB** Command >Y = IMNOISE(X, ’ gaussian,m,v) >Y = X+m+randn(size(X))*v; or Note: rand() generates random numbers uniformly distributed over [0,1] randn() generates random numbers observing Gaussian distribution N(0,1) EE465: Introduction to Digital **Image** Processing28 **Image** Denoising Noisy **image** Y filtering algorithm Question: Why not **use** median filtering? Hint: the noise type has changed. X ^ denoised **image** EE465: Introduction/

independent Simple to **use**. FPGA Design – verification Take an **image** **MATLAB** Make it into a stream files Send it to simulator Receive the simulator output vector stream Verified in **MATLAB** environment VHDL model result Vs **Matlab** model result. Research/192, University Of Technology RWTH Aachen, Germany. [10] L.Lin, G.B. Adams II, E.J. Coyle, “ Input **Compression** and Efficient Algorithms and Architectures for Stack filters ”. IEEE proc. Winter Workshop on non linear digital signal processing, Tempere Finland pp./

REC and GSAU in an ultrasound system **using** **MATLAB** and Field II. 2. To accelerate the GSAU algorithm **using** a graphics processing unit (GPU) to achieve real-time processing of the **images**. 6 Outline I. Motivation & project/, apodized, and excited ultrasound transducers,” IEEE Trans. Ultrason., Ferroelec. and Freq. Contr. Ultrasonic **Imaging** **using** Resolution Enhancement **Compression** and GPU- Accelerated Synthetic Aperture Techniques Presenter: Anthony Podkowa May 2, 2013 Advisor: Dr José R. Sá/

This is a linear programming problem and can be solved with any LP solver (in **Matlab**, for example) or with packages like L1-magic. Signal Reconstruction The first **use** of L1-norm for signal reconstruction goes back to a PhD thesis in 1965 – by / varied from 1000 to 30000. Note that this is equivalent to **using** a sensing matrix of size m by N. **Compressive** Sensing: Toy Example with **images** In the second method, we reconstruct the **image** **using** the first 1000 largest DCT coefficients and a random selection of the/

(ROIs) from the filtered, **compressed** **images**. Utilize prior knowledge about shape change dynamics to segment noisy/low contrast imagery. Make this process fast enough to run in real-time, **using** only current and past **images** for segmentation. Concept sketch / /functional Requirements: NFR001: The program shall be written in C++. NFR002: The program shall run faster than in **matlab**. NFR003: The program shall be capable of running in real time. Deliverables The deliverables of our project include/

all rights reserved. 4 Client Needs MR **Image** Reconstruction **using** KFCS Kalman Filtered **Compressed** Sensing CS: relies on data sparseness (in some domain) to estimate signal **using** fewer observations/ measurements Fewer measurements faster reconstruction Port existing **MATLAB** reconstruction prototype algorithm for runtime improvements Develop algorithm for sequential **image** segmentation Sequentially segment deforming objects (ROIs) from **images** Utilize prior knowledge about shape change dynamics/

) y 1 and y 2 are less correlated p(y 1 y 2 ) p(y 1 )p(y 2 ) Please **use** **MATLAB** demo program to help your understanding why it is desirable to have less correlation for **image** **compression** EE465: Introduction to Digital **Image** Processing 19 Transform=Change of Coordinates Intuitively speaking, transform plays the role of facilitating the source modeling Due to the decorrelating/

q is an integer between 0 and 100 ( the lower the number the higher the degradation due to JPEG **compression**). >>imwrite(f, ‘bubbles25.jpg’, quality,25) www.imageprocessingbook.com © 2004 R. C. Gonzalez, R. E. Woods, and S. L. Eddins Digital **Image** Processing **Using** **MATLAB** ® Chapter 2 Fundamentals Chapter 2 Fundamentals www.imageprocessingbook.com © 2004 R. C. Gonzalez, R. E. Woods, and S. L/

following algorithms **using** FPGA: Full panoramic rotation: 0 to 360 degrees Support of Zoom function Support of Crop-**Image** function Minimum **image** distortion TX Path Memory Management Memory Management RX Path SDRAM Controller WBS WBM WBS Host (**Matlab**) VGA Display IS42S16400 SDRAM WBM Display Controller Display Controller WBS WBM UART VESA Wishbone INTERCON Wishbone INTERCON 1. Editing the **Matlab** GUI to support non **compressed** **image** Old Uart/

**imaging** system II. REC III. SAFT IV. Functional requirements V. Simulation results 5 I. Ultrasonic **Imaging** System Figure 3: Block diagram **Image** reconstruction system Transducer REC (excitation) SAFT (delay processing) REC (**compression**) 6 Transducer An electro-mechanical device **used**/ Transducer shall be a linear array comprising 128 elements STA shall be performed through **MATLAB** Field II STA shall be performed through **MATLAB** Field II STA mode: synthetic transmit aperture (STA) STA mode: synthetic transmit /

-pass filtered version of x(m,n) Example (Laplacian operator) 32 **MATLAB** Implementation % Implementation of Unsharp masking function y=unsharp_masking(x,lambda) % Laplacian/**useful** to enhance the reflectance component, while reducing the contribution from the lighting component. Homomorphic Filtering Homomorphic filtering is a frequency domain filtering process that **compresses** the brightness (from the lighting condition) while enhancing the contrast (from the reflectance properties of the object). The **image**/

: http://www.fil.ion.ucl.ac.uk/spm/local/update/ Unpack the .zip archive **using** any data **compression** software In **Matlab**, modify the path to include this new directory or move into it. Type SPM_update at/a Fisher-Scoring ascent on F to find ReML variance parameter % estimates. %__________________________________________________________________________ % Copyright (C) 2005 Wellcome Department of **Imaging** Neuroscience % John Ashburner & Karl Friston % $Id: spm_reml.m 249 2005-10-05 17:58:37Z karl $ H1 Line/

A Conceptual Overview of Edge Detection Digital Barcode Reading Obstetric Ultrasound **Image** Processing for Suppressing Ribs in Chest Radiographs Mage Types. Formats and **Compression** The Hubble Telescope **Image** Processing Methods **Used** to Improve Explosive Detection Morphological **Image** Processing **using** **MATLAB** Digital Mammography Systems Tsunami-affected Areas In Moderate-resolution Satellite **Images** ROI-Based Processing on Digital Photograph 3d Seismic **Imaging** and Its Effects on the Oil & Gas Industry Dynamic/

. –MSE=var(original-decompressed) Distortion vs. Rate Results Quantization Imagery **compressed** **using** varying step sizes:2^-7 to 2^7 MSE calculated Processing time determined by **Matlab**’s tic/toc command Distortion vs. Quantization Results Time vs. Quantization Results Time vs. Spatial Resolution **Images** Sizes: –512x512 –256x256 –128x128 –64x64 Processing time determined by **Matlab**’s tic/toc command Time vs. Spatial Resolution Conclusion Rate & quantization/

linked subroutines produced from C, C++ or Fortran source code. **Useful** when dealing with non efficient-**Matlab** algorithms (e.g. iterative algorithm implemented as loops). mex –setup : Setup mex compiling configurations. Data Visualization **Useful** Commands: scatter()/plot() – **Useful** to plot points on **image**. imagesc() – **Useful** for 2D data. print() – Save figure as **image** on disk (careful with lossy **compressions**) General Tips Avoid loops Manage memory (Clear unused variables/

subtraction, the character pairs.+ and._ are not **used**. **Matlab** Arithmetic operation functions Oper.NameMatlab functionComments ---------------------------------------------- +Array/**used** when we **use** the multiplicative noise models. It is also **used** in digital watermarking to embed the watermark **image** with the cover **image** **Uses** of IPT arithmetic operators imdivide can be **used** to divide the intensity value of all the pixels of an **image** by a constant if it is too bright. imabsdiff is **useful** when we want to **compress**/

as struct arrays 9 Reading HDF5 Data with Dynamically Loaded Filter **MATLAB** can easily read datasets with dynamically loaded **compression** filters Example **using** BZIP2 compressor % Set the HDF5_PLUGIN_PATH environment variable >> setenv( HDF5_PLUGIN_PATH ,/url = http://planetarynames.wr.usgs.gov/**images**/moon_sp.jpg; >> data = webread(url); >> imshow(data) >> filename = lunarSouthPole.jpg >> options = weboptions >> options.Timeout = 10; >> options.ContentType = **image**; >> outFile = websave(filename,url,options/

DC component, the average intensity The top-left coeffs represent low frequencies, the bottom right – high frequencies **Image** **compression** **using** DCT Quantize – More coarsely for high frequencies (which also tend to have smaller values) – Many quantized/– Robustness to outliers Source: K. Grauman Median filter Salt-and-pepper noise Median filtered Source: M. Hebert **MATLAB**: medfilt2(**image**, [h w]) Median vs. Gaussian filtering 3x35x57x7 Gaussian Median Other non-linear filters Weighted median (pixels further from/

some **Matlab** examples demonstrating the **image** **compression** application of SVD. Command [U,S,V]=svd(A) factors A in **Matlab** The first example I will show you, displays side-by-side an original gray-scale **image** and a sequence of increasingly better approximations The second example prompts for the number of singular values to **use** in the **compression**. Then it displays the original **image** in figure 1, **compressed** **image** in/

We create a new object **using** ‘figure’ Key properties include – Colormap (this means we can only have 1 per window) – Size and position in screen – Real world size when saved as an **image** **Use** **Matlab** help to see full list/**compressing** formats tend to look nicer Avoid jpg’s – Vector formats (pdf, eps, etc. allow infinite zooming when included in reports) Also helps to configure line width and marker size and font size Can also **use** copy figure from menu and paste directly in Word/PowerPoint etc. Saving **images** **Use**/

DC component, the average intensity The top-left coeffs represent low frequencies, the bottom right – high frequencies **Image** **compression** **using** DCT Quantize – More coarsely for high frequencies (which also tend to have smaller values) – Many quantized/– Robustness to outliers Source: K. Grauman Median filter Salt-and-pepper noise Median filtered Source: M. Hebert **MATLAB**: medfilt2(**image**, [h w]) Median vs. Gaussian filtering 3x35x57x7 Gaussian Median Other non-linear filters Weighted median (pixels further from/

**MATLAB** **IMAGE** PROCESSING TOOLBOX Introduction **MatLab** : Matrix Laboratory A high-level language for matrix calculations, numerical analysis, & scientific computing Programming Can type on command line, or **use** a program file (“m”-file) Semicolon at end of line is optional (suppresses printing) Control flow (if, for, while, switch,etc) similar to C Differences from C: no variable declarations, no pointers **MATLAB**/(F);Fimag=**imag**(F);Fabs=abs(F);Fphs=angle(F); imshow(Freal) f=ifft2(F); DCT and **compression** I=imread(‘/

high quality: **compression** = 2.33 PhotoShop **Image** Very low quality: **compression** = 115 Produced by **MATLAB** with Quality = 0 **Image** Processing Architecture, © 2001-2004 Oleh TretiakPage 11Lecture 8 Review: Entropy Coding DCT coefficients are pre-processed before entropy coding DC coefficient is coded differently from other coefficients Non-zero coefficients are run-length coded DC coefficients are preprocessed: inter-block differences Huffman coding commonly **used** Different tables/

Digital **Image** Processing (Spring04) Lec1 – Introduction [9] **Compression** Color **image** of 600x800 pixels –Without **compression** u 600*800 * 24 bits/pixel = 11.52K bits = 1.44M bytes –After JPEG **compression** (popularly **used** on web) u only 89K bytes u **compression** ratio /Assignment E-Handout (Sec.I of Bovik’s Handbook) Introductory sections in **Matlab** **Image** Processing Toolbox –http://www.mathworks.com/access/helpdesk/help/toolbox/**images**/**images**.shtml Go over mathematical preliminaries –Linear system and basics of 1-D /

**Image** Processing with **MATLAB** ® Asia Edition McAndrew ‧ Wang ‧ Tseng Chapter 2: **Images** and M ATLAB 2 © 2010 Cengage Learning Engineering. All Rights Reserved. 2.1 Grayscale **Images** M ATLAB is a data analysis software package with powerful support for matrices and matrix operations Command window Reads pixel values from an **image**/, include the size of the **image** in pixels (height and width) It may also include the color map, **compression** **used**, and a description of the **image** 2.5 **Images** Files and Formats Ch2-p.29/

algorithm implemented as loops). mex –setup : Setup mex compiling configurations. MEX - **MATLAB** Executable Data Visualization **Useful** Commands: scatter() – **Useful** to plot points on **image**. Imagesc() – **Useful** for 2D data. print() – Save figure as **image** on disk (careful with lossy **compressions**) Data Visualization General Tips Avoid loops Manage memory (Clear unused variables) **Useful** command: clearvars() Avoid memory duplication – **use** nested functions function myfun A = magic(500); function setrowval(row, value/

G, B, A) values. Digital **images** can be processed in various ways: **compression** restoration, de-blurring enhancement, noise reduction **Image** processing original **image** restored **image** **Image** merging input output **Image** merging – a more complex example Input **images** output Example taken from http://www./ flow data might have coordinate units of inches and data units of psi. A number of **MATLAB** functions are **useful** for visualizing scalar data: Slice planes provide a way to explore the distribution of data values within/

functions. 3. Bit-Plane Slicing- programmed example: apply bit-plane slicing in **Matlab** to read cameraman **image** , then extract the **image** of bit 6. Solution: x=imread(cameraman.tif); y=x*0; [w/nk : # of pixels with having gray level rk We manipulate Histogram for **image** enhancement. Histogram data is **useful** in many applications like **image** **compression** and segmentaion. Histogram of the **image**: Ch3, lesson 4: histogram Histogram of the **image**: histogram هو تمثيل لعدد البكسل في كل قيمة لونية من درجات gray levels h/

= U Λ V T = σ1σ1 σ2σ2 σ3σ3 **Use** svd command in **MATLAB**… Original (N=350)**Compressed** (N=100) Singular Value Decomposition for **Image** **Compression** Original (N=350)**Compressed** (N=75) Singular Value Decomposition for **Image** **Compression** Original (N=350)**Compressed** (N=50) Singular Value Decomposition for **Image** **Compression** Original (N=350)**Compressed** (N=25) Singular Value Decomposition for **Image** **Compression** x Clicking on a node flips the color of that node and/

**Compression** (REC) 24 WHY REC? Figure 9: Resolution Comparison [3]Figure 10: Background-target separation [3] Before REC, conventional pulsing (CP) was **used** Before REC, conventional pulsing (CP) was **used** CP proved ineffective in term of **image** resolution CP proved ineffective in term of **image** resolution 25 WHY REC? To enhance **image**/ of a linear array of elements SAF shall be performed through **MATLAB** Field II. SAF shall be performed through **MATLAB** Field II. Total memory consumption shall not > 2 gigabytes. /

Abstract Output In this project, we develop a **MATLAB** model that simulates infrared (IR) **images** of the Earth from old IR weather **images**. These **images** are necessary to develop and test data **compression** algorithms for the **images** of Earth taken from space **using** an IR camera. None of the existing databases of IR **images** consider the factors like the location of the satellite, orientation of the camera, etc in/

Spring 2015 z.r.ghassabi@gmail.com Digital **Image** Processing Morphological operation 1 Outline Introduction Digital **Image** Fundamentals Intensity Transformations and Spatial Filtering Filtering in the Frequency Domain **Image** Restoration and Reconstruction Color **Image** Processing Wavelets and Multi resolution Processing **Image** **Compression** Morphological Operation Object representation Object recognition 2 **Used** to extract **image** components that are **useful** in the representation and description of region shape/

Spring 2015 z.r.ghassabi@gmail.com Digital **Image** Processing Morphological operation 1 Outline Introduction Digital **Image** Fundamentals Intensity Transformations and Spatial Filtering Filtering in the Frequency Domain **Image** Restoration and Reconstruction Color **Image** Processing Wavelets and Multi resolution Processing **Image** **Compression** Morphological Operation Object representation Object recognition 2 **Used** to extract **image** components that are **useful** in the representation and description of region shape/

**Image** Processing EE465: Introduction to Digital **Image** Processing Range **Compression** x L c=100 EE465: Introduction to Digital **Image** Processing Summary of Point Operation So far, we have discussed various forms of mapping function f(x) that leads to different enhancement results **MATLAB**/ McMillan. “Video enhancement **using** per-pixel virtual exposures,” In ACM SIGGRAPH 2005 EE465: Introduction to Digital **Image** Processing EE465: Introduction to Digital **Image** Processing **Image** Enhancement Introduction Spatial domain /

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