EECS225B, Fall 2023
Digital Image Processing
Wednesdays and Fridays, 11:00am-12:29pm @ Cory 521
Required Text:
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R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 4th Edition.
Video lectures:
EE225B, Spring 2006
Course Details:
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Lecturer:
Professor Avideh Zakhor
avz@eecs.berkeley.edu
507 Cory Hall
Phone: (510) 643-6777
Office Hours: Fridays 12:30pm-1:30pm
TAs:
Chin-An (Daniel) Chen
chinanchen@berkeley.edu
Lance Mathias
lmathias@berkeley.edu
Han Cui
louiscuihan2018@berkeley.edu
Office Hours: Announced on Ed
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Recommended Books:
- Prof. Hany Farid's notes on Fundamentals of Image Processing. PDF Video
- Bovik, Handbook of Image and Video Processing, Academic Press 2000.
- N. Netravali and Barry G. Haskell, Digital Pictures, Plenum Press, 1988.
- W.K.Pratt, Digital Image Processing, John Wiley and Sons, 1992.
- A.M. Tekalp, Digital Video Processing, Prentice Hall, 1995.
- Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. PDF
- Ponce, Jean, et al. Computer vision: a modern approach. Computer 16.11 (2011). PDF
Other useful resources online:
- Deep Learning: CS 182 Spring 2021 [Videos on Youtube] [Lecture Slides on Github]
- Fundamentals of Digital Image and Video Processing
- Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital
- 22 videos on deep learning for computer vision, Justin Johnson, University of Michigan
Deep learning Restoration and Enhancement papers from ICIP 2022:
- IMAGE DEBLURRING USING DEEP MULTI-SCALE DISTORTION PRIOR
- DEEP IMAGE DEBANDING
- ATTENTION-BASED NEURAL NETWORK FOR ILL-EXPOSED IMAGE CORRECTION
- A NEW REGULARIZATION FOR RETINEX DECOMPOSITION OF LOW-LIGHT IMAGES
- SVBR-NET: A NON-BLIND SPATIALLY VARYING DEFOCUS BLUR REMOVAL NETWORK
Outline of Topics:
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Introduction
- Applications
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Elements of visual perception, Structure of the human eye, Image Formation in the Eye, Brightness Adaptation and Discrimination
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Light and Electromagnetic Spectrum
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Image sensing and acquisition
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Sampling and quantization
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Spatial transformations; intensity transformations and spatial filtering; Linear filters; order statistics nonlinear filters
- Frequency domain filtering, sharpening filters; homomorphic filtering
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Image enhancement; histogram processing, equalization, matching
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Basics of deep learning; variations of neural networks; CNNs and GANS
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Applications to classification, object detection, semantic segmentation
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Image restoration; noise models; order statistics filters; adaptive filters
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Estimation of degradation function; Weiner filtering; inverse filtering; constrained least squares filtering
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Restoration using deep neural networks
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X-ray tomography, projections, radon transform
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Reconstruction from projections; Fourier slice theorem; back-projections
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Tomography using deep neural networks
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Continuous and discrete wavelet transform and relationship to subband coding
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Image compression basics
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What to code, space domain coding; transform coding, DCT, wavelet transform
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Linear and nonlinear quantization
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Bit allocation: entropy; Huffman coding; arithmetic coding
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JPEG 2000 basics; spatial scalability; bit rate scalability
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Motion estimation, video coding basics, video standards
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Applications of deep learning to compression
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Edge detection
- Segmentation using graph cuts
- Keypoint detectors
- Feature extraction; SIFT, MSER
- Feature matching and RANSAC
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- Morphological filtering; dilation, erosion; closing, opening
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- Super resolution: classical methods and deep neural networks
Homework:
- Homework will be issued approximately once a week.
- They will either consist of paper reviews or Python/Matlab programming assignments.
- Homework will be graded, and will contribute 60% to the final grade, where 10% is for reading assignments and 50 percent is for actual homework.
- Homework handed in late will not be accepted unless consent is obtained from the Professor prior to the due date.
- There will be a project that will constitute 35% of your grade. The project can be individual or in a group. You are to submit a proposal to the instructor by the end of October. More details on the project will be provided later, and a list of suggested topics will be provided. In addition, 5% of your grade will be for in class participation.
Project:
- There will be a project that will constitute 35% of your grade.
- The project can be individual or in a group.
- You are to submit a proposal to the instructor by the end of October.
- More details on the project will be provided later, and a list of suggested topics will be provided.
- In addition, 5% of your grade will be for in class participation.
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Welcome to EECS225B Fall 2023!
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Please sign up for the class ed page at this Link
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Wed. Aug. 23, 2023.
Lecture 1: Introduction
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Wed. Aug. 30, 2023.
Lecture 2: Modalities of Image Capture
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Fri. Sep. 1, 2023.
Lecture 3: Human Perception
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Fri. Sep. 8, 2023.
Lecture 4: Signals Systems
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Wed. Sep. 13, 2023.
Lecture 5: Intensity Transformation and Spatial Filtering
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Fri. Sep. 15, 2023.
Lecture 6: Deep Learning (1) (pdf) (ppt)
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Wed. Sep. 20, 2023.
Lecture 7: Deep Learning (2) (pdf) (ppt)
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Fri. Sep. 22, 2023.
Lecture 8: Recurrent Neural Networks
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Wed. Oct. 11, 2023.
Lecture 11: Noise Estimation and Image Restoration
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Fri. Oct. 13, 2023.
Lecture 12: Frequency Domain Filtering (1)
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Wed. Oct. 18, 2023.
Lecture 13: Frequency Domain Filtering (2)
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Fri. Oct. 20, 2023.
Lecture 14: Image Restoration and Stationary Random Processes
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Wed. Oct. 25, 2023.
Lecture 15: Inverse Filtering and Tomography (1)
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Fri. Oct. 27, 2023.
Lecture 16: Inverse Filtering and Tomography (2)
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Wed. Nov. 1, 2023.
Lecture 17: Tomography (3)
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Fri. Nov. 3, 2023.
Lecture 18: Reconstruction from Fourier Transform
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Reconstruction from Partial Fourier Information
Wed. Nov. 8, 2023.
Lecture 19: Image Coding
Methods of Bit Assignment
Wed. Nov. 15, 2023.
Lecture 20: Transform Coding, JPEG, JPEG2000
Fri. Nov. 17, 2023.
Lecture 21: Pyramid Coding
Wed. Nov. 29, 2023.
Lecture 22: STFT and Wavelets
Fri. Dec. 1, 2023.
Lecture 23: Motion Estimation and Video Compression
Fall 2022 Lecture Notes:
- Problem Set #1. Due 9/15 11:00 a.m.
- Problem Set #2. Due 9/22 11:00 a.m.
- Reading Assignment #1. Due 10/02 11:00 a.m.
- Reading Assignment #2. Due 10/09 11:00 a.m.
- Reading Assignment #3. Due 10/16 11:00 a.m.
- Problem Set #3. Due 10/25 11:00 a.m.
- Problem Set #4. Due 11/01 11:00 a.m.
- Problem Set #5. Due 11/08 11:00 a.m.
- Problem Set #6. Due 11/15 11:00 a.m.
- Problem Set #7. Due 11/22 11:00 a.m.
- Problem Set #8. Due 12/04 11:00 a.m.
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