Prof. Jaakko T. Astola
Professor, emeritus at Tampere Univ Technology
SPIE Involvement:
Fellow status | Conference Chair | Symposium Chair | Author | Editor | Instructor
Publications (167)

PROCEEDINGS ARTICLE | March 17, 2015
Proc. SPIE. 9394, Human Vision and Electronic Imaging XX
KEYWORDS: Image visualization, Fabry–Perot interferometers, Statistical analysis, Visualization, Databases, Image quality, Neural networks, Image denoising, Molybdenum, Neurons

PROCEEDINGS ARTICLE | February 19, 2013
Proc. SPIE. 8655, Image Processing: Algorithms and Systems XI
KEYWORDS: Image visualization, Image compression, Visual analytics, Visualization, Databases, Image processing, Interference (communication), Image analysis, Image quality, Molybdenum

PROCEEDINGS ARTICLE | September 11, 2012
Proc. SPIE. 8413, Speckle 2012: V International Conference on Speckle Metrology
KEYWORDS: Data modeling, Speckle, Sensors, Free space, Spatial light modulators, Speckle metrology, Image filtering, Speckle imaging, Current controlled current source, Compressed sensing

PROCEEDINGS ARTICLE | May 5, 2012
Proc. SPIE. 8429, Optical Modelling and Design II
KEYWORDS: Sensors, Graphics processing units, Matrices, Image processing, Spatial light modulators, Phase retrieval, Wave propagation, Free space optics, Reconstruction algorithms, Algorithm development

PROCEEDINGS ARTICLE | February 2, 2012
Proc. SPIE. 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
KEYWORDS: Image visualization, Image compression, Visualization, Databases, Image processing, Photography, Discrete wavelet transforms, Image analysis, Image quality, Molybdenum

PROCEEDINGS ARTICLE | February 2, 2012
Proc. SPIE. 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
KEYWORDS: Optical filters, Visualization, Databases, Image processing, Digital filtering, Denoising, Image quality, Image filtering, Nonlinear filtering, 3D image processing

Showing 5 of 167 publications
Conference Committee Involvement (28)
Mathematics of Data/Image Pattern Coding, Compression, and Encryption with Applications XIV
21 August 2011 | San Diego, California, United States
Image Processing: Algorithms and Systems IX
24 January 2011 | San Francisco Airport, California, United States
Mathematics of Data/Image Pattern Recognition, Compression, and Encryption, with Applications XIII
3 August 2010 | San Diego, California, United States
Image Processing: Algorithms and Systems VIII
19 January 2010 | San Jose, California, United States
Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII
3 August 2009 | San Diego, California, United States
Showing 5 of 28 published special sections
Course Instructor
SC761: Novel Spatially Adaptive Anisotropic Local Approximation Techniques in Image Processing
This half-day course presents a practical and application oriented overview of novel advanced image processing algorithms. Briefly the idea is as follows: the concept of adaptive local polynomial approximation (LPA) has been developed to deal with anisotropic signals. This type of method searches for a largest local star-shaped neighborhood where LPA fits well to data. It is typically applied in a point-wise manner and defines a nonlinear varying scale (window size and shape) adaptive filter. This adaptation is based on recent adaptive estimation results that have been obtained in mathematical statistics. Local versions of the local maximum/quasi likelihood are used for non-Gaussian models. Special algorithms have been designed for photon-limited imaging based on the Poisson distribution of data. Overall, the techniques covered in the course belong to the general class of nonlinear spatially adaptive filters and they demonstrate state-of-art performance and on many occasions visually and quantitatively outperform the best methods currently in use. A wide scope of imaging problems is considered: denoising Gaussian and non-Gaussian images, non-blind deblurring, blind multichannel deblurring, super-resolution imaging, denoising poissonian signals, multiresolution imaging, edge detection, color imaging, etc. The algorithms are implemented in Matlab codes and based on efficient frequency domain calculations.
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