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Institutionernas kurser för doktorander

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Kursplan för

Läsår
RRY025 - Image processing
 
Kursplanen fastställd 2012-02-21 av programansvarig (eller motsvarande)
Ägare: MPWPS
7,5 Poäng
Betygskala: TH - Fem, Fyra, Tre, Underkänt
Utbildningsnivå: Avancerad nivå
Huvudområde: Elektroteknik, Teknisk fysik
Institution: 75 - RYMD- OCH GEOVETENSKAP


Undervisningsspråk: Engelska
Sökbar för utbytesstudenter
Blockschema: C

Modul   Poängfördelning   Tentamensdatum
Lp1 Lp2 Lp3 Lp4 Sommarkurs Ej Lp
0107 Tentamen 7,5hp Betygskala: TH   7,5hp   23 Okt 2012 em V,  15 Jan 2013 em M,  23 Aug 2013 em M

I program

MPWPS WIRELESS, PHOTONICS AND SPACE ENGINEERING, MSC PROGR, Årskurs 2 (valbar)
MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Årskurs 2 (valbar)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Årskurs 2 (valbar)
MPCOM COMMUNICATION ENGINEERING, MSC PROGR, Årskurs 2 (obligatoriskt valbar)
MPPAS PHYSICS AND ASTRONOMY, MSC PROGR, Årskurs 2 (valbar)

Examinator:

Docent  Alessandro Romeo


Kursutvärdering:

http://document.chalmers.se/doc/00000000-0000-0000-0000-000010702810


Behörighet:

För kurser inom Chalmers utbildningsprogram gäller samma behörighetskrav som till de(t) program kursen ingår i.

Kursspecifika förkunskaper

Basic knowledge in mathematics and programming skills

Syfte

The aim of this course is for students to become familiar with
a wide variety of techniques in modern Image Processing. These
techniques can be used to subjectively improve image quality
for the end-user (image enhancement), remove known image distortions
(image restoration) and to reduce image data sizes for storage or
transmission (image compression). These techniques are valuable in a
range of applications and careers including, but not limited to,
medical imaging, astronomy, remote sensing, automation etc. Stress is
placed on deep understanding of the principles underlying the techniques
rather than memory learning of algorithms.

Lärandemål (efter fullgjord kurs ska studenten kunna)


  • Visualise via means of mental images the process of forming 1D and
    2D Fourier transforms and also the convolution process. Describe the
    similarities and differences between the continuous and discrete
    Fourier transforms and their inter-relationship.

  • Select and apply appropriate image enhancement methods
    for different applications. Discriminate between cases where automated
    image enhancement methods produce appropriate results and
    where they do not.

  • Understand the differences between averaging and median filtering for
    reducing image noise.

  • Demonstrate understanding of image smoothing and sharpening
    in both the image and Fourier domains. Select between optimum methods
    of edge detection in different applications.

  • Describe common distorted images as convolutions of the true image with
    point spread functions (PSF). Describe and decide under which conditions
    different image restoration algorithms can be used and describe
    the strengths and weakness of these algorithms.

  • Describe the Cosine transform and its relationship
    to the Fourier transform.

  • Demonstrate a basic understanding of wavelets and know how to use them to
    compress and denoise data.

  • Explain the difference between lossy and lossless compression methods
    and explain the concept of data redundancy as the source of compression.
    Describe the subcomponents of general compressor/decompressor algorithms
    Calculate theoretical limits to lossless compression using the Shannon
    noiseless coding theorem and implement Huffman coding.

  • Describe a variety of different mapping functions that can be used to
    obtain compression and decide when different methods are appropriate.
    Show via examples why Digital Pulse Code Modulation (DPCM) works and
    is stable in the face of quantisation errors.

  • Describe the main components of the JPEG standard.

  • Write computer code in MATLAB to implement
    selected image processing algorithms.

Innehåll

Introduction.  Image Enhancement: transform functions, and histogram
equalisation; image smoothing and sharpening; edge detection and noise
reduction; Fourier domain methods.  Continuous and Discrete 2D Fourier
Transforms.  Wavelets and Wavelet Applications.  Image Compression:
general compressor/decompressor, coding theorem, Huffman coding and
multi-pixel coding; run length coding, predictive coding and digital pulse
code modulation; cosine transform, block coding, zonal mask and threshold
mask; JPEG.  Image Restoration: linear space-invariant distortions, point
spread function, inverse and pseudoinverse filters; Wiener filter; image
reconstruction from projections.

Organisation

Lectures, lab exercises, problem classes and project.

Litteratur

'Digital Image Processing' 3rd edition (2008) by
Gonzalez and Woods.

Examination

Project and written exam.


Sidansvarig Publicerad: må 13 jul 2020.