Advanced Image Enhancement and Deblurring
Instructor: Majid Rabbani, Rochester Institute of Technology
Level: Advanced
Duration: 4 Hours plus 30-minute break and 30-minute post-class discussion
Course Time:
    New York: Wednesday 13 January, 10:00 – 15:00
    Paris: Wednesday 13 January, 16:00 – 21:00
    Tokyo: Thursday 14 January, 00:00 – 05:00

Prerequisites: Knowledge of basic image processing and linear systems (e.g., convolution, Fourier Transforms, etc.) would be very useful.

Benefits
This course enables the attendee to:

• Understand advanced algorithms used for contrast enhancement such as CLAHE, Photoshop Shadows/Highlights, and Dynamic Range Compression (DRC).
• Understand advanced techniques used in image sharpening such as variations of nonlinear unsharp masking.
• Understand advanced techniques used in image noise removal such as bilateral filtering and CNN-based techniques.
• Understand how motion information can be utilized in image sequences to improve the performance of various enhancement techniques such as sharpening, noise removal, and artifact removal.
• Understand Wiener filtering and its variations for image deblurring (restoration).

This course discusses some of the advanced algorithms used for contrast enhancement, noise reduction, sharpening and deblurring of still images and video. Applications include consumer and professional imaging, medical imaging, forensic imaging, surveillance, and astronomical imaging. Image examples complement the technical descriptions.

Intended Audience
Scientists, engineers, and technical managers who need to understand and/or apply the techniques employed in digital image processing in various products in a diverse set of applications such as medical imaging, professional and consumer imaging, forensic imaging, will benefit from this course. Some knowledge of digital filtering (convolution) and frequency decomposition is necessary for understanding the deblurring concepts.

Majid Rabbani has 38 years of experience in digital imaging. After a 33-year career at Kodak Research labs, he retired in 2016 with the rank of Kodak Fellow. Currently, he is a visiting professor at the EE department of Rochester Institute of Technology (RIT). He is a twice co-recipient of the Kodak C. E. K. Mees Research Award and the co-recipient of two Engineering Emmy Awards. He is a Fellow of IEEE (1997), a Fellow of SPIE (1993), a Kodak Distinguished Inventor with 44 issued patents, and the 2015 recipient of the Electronic Imaging Scientist of the Year Award. He has been an active educator in the digital imaging community for the past 37 years.

COST

Discounts given for multiple classes. See Registration page for details and to register.

For office use only: