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, which has applications in biomedical images, time-varying imagery, robotics, and optics. The KLA-Tencor judges in attendance this year were Eliezer Rosengaus and Jing Zhang.
|Rebecca Malinas, Yaohui Li, Abhishek Bafna, Tatyana Dobrev|
"In many imaging applications," explained the students, "an imaging system may produce low-resolution, blurry images. It is possible to improve the resolution of the output images through a process called super-resolution, in which we use multiple low-resolution images to recover a high-resolution image. However, the captured low-resolution images may also be blurry and noisy, which necessitates a deblurring step to recover a clear, high-resolution image. There is an additional problem with this, in that we do not know the blur kernel and so must simultaneously estimate the high-resolution image and the blur kernel. In our project, we investigated these processes and improved them through the use of color information in images. The use of color information greatly improved results."
|Comparison of the original blurry image and the image improved through super-resolution|
|Comparison of the original blurry image and the image improved through super-resolution, with the addition of color information|
|Zhen Zeng, Lianli Liu, Jie Li, Jiyang Chu|
In magnetic resonance imaging (MRI), intensity nonuniformities, also known as the bias field, arise from a variety of factors. The human eye is able to taken into account this intensity inhomogeneity, allowing medical experts to analyze the MRI correctly. However, many intensity based image analysis algorithms are very sensitive to such intensity variation; thus correction of bias field is of great importance for accurate image analysis results. This project applied state-of-the-art segmentation techniques developed in computer vision to obtain better bias field estimation, and improved MRI images.
|Input: input image with bias field||Input: ground truth tissue label||Labeling of proposed method||Labeling of fuzzy c-means|
|Ground truth image||Corrected image by proposed method||Corrected image by fuzzy c-means|