Image processing II

MICRO-512 / 3 credits

Teacher(s): Liebling Michael Stefan Daniel, Sage Daniel, Unser Michaël, Van De Ville Dimitri Nestor Alice

Language: English

Summary

Study of advanced image processing; mathematical imaging. Development of image-processing software and prototyping in Jupyter Notebooks; application to real-world examples in industrial vision and biomedical imaging.

Content

  • Directional image analysis. Mathematical foundations. Structure tensor. Steerable filters.
  • Continuous representation of discrete data. Splines. Interpolation. Geometric transformations. Multi-scale decomposition (pyramids and wavelets).
  • Image transforms. Karhunen-Loève transform (KLT). Discrete cosine transform (DCT). JPEG coding. Image pyramids. Wavelet decomposition.
  • Reconstruction in the continuum. Wiener filter. Radon transform. Fourier slice theorem. Filtered backprojection.
  • Computational imaging. Imaging as an inverse problem. Iterative reconstruction methods. Elements of convex analysis. Regularization & sparsity constraints.

Learning Prerequisites

Required courses


Image Processing I


Recommended courses
Signals and Systems I & II, linear algebra, analysis


Important concepts to start the course
Basic image processing and related analytical tools (Fourier transform, z-tranform, etc.)

Recommended courses

Signals and Systems I & II, linear algebra, analysis

Important concepts to start the course

Basic image processing and related analytical tools (Fourier transform, z-tranform, etc.)

Learning Outcomes

  • Construct interpolation models and continuous-discrete representations
  • Analyze image transforms
  • Design image-reconstruction algorithms
  • Formalize multiresolution representations using wavelets
  • Design deconvolution algorithms
  • Perform image analysis and feature extraction
  • Design image-processing software (plugins)
  • Synthesize steerable filters
  • Construct interpolation models and continuous-discrete representations
  • Analyze image transforms
  • Design image-reconstruction algorithms
  • Formalize multiresolution representations using wavelets
  • Perform image analysis and feature extraction
  • Design image-processing software
  • Design image reconstruction algorithms

Transversal skills

  • Plan and carry out activities in a way which makes optimal use of available time and other resources.
  • Manage priorities.
  • Access and evaluate appropriate sources of information.
  • Use both general and domain specific IT resources and tools

Assessment methods

The objectives of the course will be assessed as follows:

  • 70% final exam
  • 30% IP labs

Resources

Moodle Link

In the programs

  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Exam form: Written (summer session)
  • Subject examined: Image processing II
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Type: optional

Reference week

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