We derive a novel method for automatic determination of the regularization parameters applicable for the class of simultaneous super-resolution (SR) algorithms. The proposed method is based on the classical joint maximum a posteriori (JMAP) estimation technique, which is a fast alternative to estimate the parameters. Unfortunately, the classical JMAP technique can be unstable and generates multiple local minima. In order to stabilize the JMAP estimation, while achieving a cost function with a unique global solution, we derive an improved solution by modeling the JMAP hyperparameters with a gamma prior distribution. Experimental results illustrate the effectiveness of the proposed method for automatic determination of the regularization parameters for the simultaneous SR. We also contrast the proposed method to a reference method named KNOWN. KNOWN is a MAP based simultaneous SR algorithm where the parameters are fixed, either known a priori or extracted from the high-resolution frames which are not usually available in practice. 1.

I have been enjoying my Ph.D. program in Ann Arbor a lot not only because of all the interesting research topics, but also because of all people I have in my life. Without them, I wouldn’t be able to survive and finish this program. I would like to thank God for sending me to Michigan and orchestrating my life among all people I met in Ann Arbor. The personal walk with Him was the source of my strength for the last 6 years. I thank my late father, Ye Ki Jun, for supporting me in many different ways. Without his promise to support me for a year, I wouldn’t be able to come to the United States. I know that it would be him to be the happiest person for my Ph.D., but I am sad that he couldn’t enjoy this moment with my family. Many people will miss him. However, I am still thankful that I can share my joy with my family, my mother Soon Ja Park, my sister Mi Hyun Jeon, my brother-in-law Masafumi Fuchikami, and my nephew Harumitsu Fuchikami. Family gatherings in Korea and Japan always cheered me up. I would like to thank my advisor Jeff Fessler for many things. I have been really enjoying working with him. His intuition for the problem, his passion for the research, and his openness to any possibilities such as my naive ideas have inspired me and I’ve learned a lot from him. It has been my privilege to work with him. I would also like to thank