The Michigan Engineer News Center

Students earn prizes for improving image processing techniques in EECS 556

The course covers the theory and application of digital image processing, with applications in biomedical images, time-varying imagery, robotics, and optics. | Short Read

Two teams earned prizes in the graduate level course, EECS 556: Image Processing, thanks to the sponsorship of KLA-Tencor. The course, taught this past term by Prof. Jeff Fessler, covers the theory and application of digital image processing, with applications in biomedical images, time-varying imagery, robotics, and optics. The KLA-Tencor judges in attendance this year were Jing Zheng and Mohan Mahadevan.

First place
Fast Partitioning of Vector-Valued Images and 3D Volumes, by Jon Macoskey, Steven Parkison, Josiah Simeth

Enlargefirst place team
IMAGE:  (L) Prof. Jeff Fessler, Josiah Simeth, Steven Parkison, Jon Macoskey, and KLA-Tencor researchers Jing Zheng and Mohan Mahadevan

In applications from medical imaging to self-driving cars, the process of partitioning an image into meaningful segments is useful. Forming these segments involves a balance between fidelity to the original image and segmentation into a reasonable number of partitions, while solving the problem in a computationally practical way. This project involved the implementation, and expansion of an efficient alternating direction method of multipliers (ADMM) approach to this problem.

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IMAGE:  Example segmentation of an image containing a collection of objects using the ADMM approach
Enlargesegmentation using ADMM
IMAGE:  Example segmentations of images containing a natural scene using the ADMM approach

Second place
High dynamic range image tone mapping using a local edge-preserving multiscale decomposition, by Hongki Lim, Wonhui Kim

Enlargesecond place team
IMAGE:  (L) Prof. Jeff Fessler, Wonhui Kim, Hongki Lim, and KLA-Tencor researchers Jing Zheng and Mohan Mahadevan

A High Dynamic Range (HDR) image has a large ratio between the maximum and minimum intensities of the image. Since it usually exceeds the dynamic range of standard displays, tone mapping process is required. The key to HDR tone mapping is to preserve details while compressing the unimportant image components. Therefore, most state-of-the-art approaches to HDR tone mapping involve separating an image into base and detail layers.

Enlargeimage before correction
IMAGE:  Original image
EnlargeHDR image after correction
IMAGE:  The resulting tone-mapped LDR image

Base layer can be obtained by applying smoothing filter to the image, which usually causes the artifacts around edges. A general solution is to formulate the energy minimization problem in terms of base layer with adaptive edge-preserving penalty term. In this project, we propose the joint base-detail decomposition by considering additional constraints on detail layers, which gives increase in both sharpness and naturalness to the resulting tone-mapped image.

first place team
segmentation using ADMM
second place team
image before correction
HDR image after correction
Portrait of Catharine June

Contact

Catharine June
ECE Communications and Marketing Manager

Electrical Engineering and Computer Science

(734) 936-2965

3301 EECS

The electrons absorb laser light and set up “momentum combs” (the hills) spanning the energy valleys within the material (the red line). When the electrons have an energy allowed by the quantum mechanical structure of the material—and also touch the edge of the valley—they emit light. This is why some teeth of the combs are bright and some are dark. By measuring the emitted light and precisely locating its source, the research mapped out the energy valleys in a 2D crystal of tungsten diselenide. Credit: Markus Borsch, Quantum Science Theory Lab, University of Michigan.

Mapping quantum structures with light to unlock their capabilities

Rather than installing new “2D” semiconductors in devices to see what they can do, this new method puts them through their paces with lasers and light detectors. | Medium Read