University of California, Berkeley
Electrical Engineering and Computer Sciences Department
Course Details | Announcements | Lecture Notes | Homework |
 

EECS225B, Fall 2023
Digital Image Processing

Wednesdays and Fridays, 11:00am-12:29pm @ Cory 521

Required Text:

  1. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 4th Edition.

Video lectures:
EE225B, Spring 2006

Course Details:

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

Recommended Books:

  1. Prof. Hany Farid's notes on Fundamentals of Image Processing. PDF Video
  2. Bovik, Handbook of Image and Video Processing, Academic Press 2000.
  3. N. Netravali and Barry G. Haskell, Digital Pictures, Plenum Press, 1988.
  4. W.K.Pratt, Digital Image Processing, John Wiley and Sons, 1992.
  5. A.M. Tekalp, Digital Video Processing, Prentice Hall, 1995.
  6. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. PDF
  7. Ponce, Jean, et al. Computer vision: a modern approach. Computer 16.11 (2011). PDF

Other useful resources online:

  1. Deep Learning: CS 182 Spring 2021 [Videos on Youtube] [Lecture Slides on Github]
  2. Fundamentals of Digital Image and Video Processing
  3. Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital
  4. 22 videos on deep learning for computer vision, Justin Johnson, University of Michigan

Deep learning Restoration and Enhancement papers from ICIP 2022:

  1. IMAGE DEBLURRING USING DEEP MULTI-SCALE DISTORTION PRIOR
  2. DEEP IMAGE DEBANDING
  3. ATTENTION-BASED NEURAL NETWORK FOR ILL-EXPOSED IMAGE CORRECTION
  4. A NEW REGULARIZATION FOR RETINEX DECOMPOSITION OF LOW-LIGHT IMAGES
  5. SVBR-NET: A NON-BLIND SPATIALLY VARYING DEFOCUS BLUR REMOVAL NETWORK

Outline of Topics:

    • Introduction
    • Applications
    • Elements of visual perception, Structure of the human eye, Image Formation in the Eye, Brightness Adaptation and Discrimination
    • Light and Electromagnetic Spectrum
    • Image sensing and acquisition
    • Sampling and quantization
    • Spatial transformations; intensity transformations and spatial filtering; Linear filters; order statistics nonlinear filters
    • Frequency domain filtering, sharpening filters; homomorphic filtering
    • Image enhancement; histogram processing, equalization, matching
    • Basics of deep learning; variations of neural networks; CNNs and GANS
    • Applications to classification, object detection, semantic segmentation
    • Image restoration; noise models; order statistics filters; adaptive filters
    • Estimation of degradation function; Weiner filtering; inverse filtering; constrained least squares filtering
    • Restoration using deep neural networks
    • X-ray tomography, projections, radon transform
    • Reconstruction from projections; Fourier slice theorem; back-projections
    • Tomography using deep neural networks
    • Continuous and discrete wavelet transform and relationship to subband coding
    • Image compression basics
    • What to code, space domain coding; transform coding, DCT, wavelet transform
    • Linear and nonlinear quantization
    • Bit allocation: entropy; Huffman coding; arithmetic coding
    • JPEG 2000 basics; spatial scalability; bit rate scalability
    • Motion estimation, video coding basics, video standards
    • Applications of deep learning to compression
    • Edge detection
    • Segmentation using graph cuts
    • Keypoint detectors
    • Feature extraction; SIFT, MSER
    • Feature matching and RANSAC
    • Morphological filtering; dilation, erosion; closing, opening
    • 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.

Announcements:

  • Welcome to EECS225B Fall 2023!
  • Please sign up for the class ed page at this Link
Lecture Notes:
Fall 2022 Lecture Notes:
Homework Assignments: