ECE 439 Digital Image
- Give the students a general
understanding of the fundamentals of digital image processing.
- Introduce the student to
analytical tools which are currently used in digital image processing as
applied to image information for human viewing.
- Develop the students ability to apply these tools in the laboratory
in image restoration, enhancement and compression.
By Test 1, the students should:
- Understand differences
between computer vision and image processing.
- Know the basic components of
an image processing system.
- Understand the basics of the
human visual system as they relate to image processing; including spatial
frequency resolution and brightness adaption.
- Understand how images are
represented; including optical images, analog images, and digital images.
Understand image types such as binary images, gray-scale images, color and
- Know the key concepts in
image file formats.
- Understand the model for an
image analysis process.
- Understand why preprocessing
is performed and know about image geometry, convolution masks, image
algebra and basic spatial filters.
- Understand image quantization
in both the spatial and brightness domains.
- Understand how discrete
transforms work; including concepts of basis images, orthogonality,
- Know about the 2-D Fourier,
discrete cosine, Walsh-Hadamard and wavelet
transforms; including implied symmetry, phase, circular
convolution, vector inner and outer products and filtering.
- Know why log remapping is
necessary for viewing spectral image data.
- Understand lowpass, highpass,
bandpass, notch filters; including ideal and
non-ideal filters such as the Butterworth.
By Test 2, the students should:
- Know the three categories of
image processing applications: restoration, enhancement and compression.
- Know how to manipulate
histograms for image enhancement; including histogram stretching,
shrinking, equalization and specification. Understand the corresponding
algorithms and equations.
- Understand gray-scale
modification, how it relates to histogram manipulation and understand
their mapping equations.
- Understand adaptive contrast
enhancement filters and their equations.
- Understand the uses of
pseudo-color. Know how to use it in both the spatial and frequency
- Understand image sharpening
concepts - how it is done in both the spatial and frequency domains.
- Know commonly used image
sharpening algorithms; including highpass, high frequency emphasis and homomorphic filtering.
- Understand the concepts of unsharp masking and how to develop an
application-specific sharpening algorithm.
- Understand image smoothing in
both the spatial and spectral domains.
- Know the system model for
image restoration, and appreciate the differences bewten
restoration and enhancement.
- Know how to use filters, both
spatial and frequency, to mitigate the effects of noise in images.
Including gaussian, uniform, gamma, Rayleigh and salt & pepper noise.
- Understand order filters and
- Understand adaptive filter
and concepts and uses.
- Know the basics of developing
a degradation model and understand the concept of a point spread function
- Understand the following
frequency domain restoration filters and their mathematical models:
inverse, Wiener, constrained least squares, geometric mean, power spectrum
equalization, parametric Wiener, notch.
- Understand geometric
transforms: including concepts of spatial transformation, gray-level
interpolation, bilinear mapping, tiepoints and their meshes.
- Know the basics of image
compression and decompression; including compression ratio, mapping,
quantization and coding. Know the advantages and disadvantages of lossy
and lossless compression.
- Know the differences between
objective and subjective image fidelity criteria, including advantages and
disadvantages of both.
- Understand error measures in
image fidelity; including RMS, total and peak error.
- Understand the concept of
entropy and its relation to image compression.
- Learn about the following
compression and coding schemes: huffman, run
length coding, LZW coding, arithmetic coding,
block truncation coding, vector quantization, differential predictive
coding, transform coding, zonal coding, JPEG and hybrid compression
By the final project in the laboratory:
- Be able to use CVIPtools to solve
image processing problems.
- Be able to write simple C
functions using the CVIPtools libraries.
- Be able to develop
application-specific algorithms for image processing.
- Have a practical and visual
understanding of the Fourier transform properties of translation, rotation
- Have a practical and visual
understanding of filtering with the FFT, DCT and Walsh transforms.
Including log remapping, use of various block sizes, implied symmetry,
ideal versus butterworth filters, and Fourier