ECE 439 Digital Image Processing

Goals:

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.

Instructional Objectives:

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 multi-spectral images.
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, orthonormality, separability and reversibility.
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 domains.
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 their uses.
Understand adaptive filter and concepts and uses.
Know the basics of developing a degradation model and understand the concept of a point spread function (PSF).
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 methods.

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 and convolution.
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 phase.