Image filtering is a fundamental task in computer vision and image processing. Various linear and nonlinear filters are routinely used for enhancement, superresolution, sharpening, restoration, etc. The focus of this thesis is on kernel-based filtering that has received significant attention in recent years.

Matching images to a discrete camera is of significance in forensic investigation. In the case of digital images, forensic matching is possible through the use of sensor noise present within every image. There exist misconceptions, however, around how this noise reacts under variables such as temperature and the use of different lens systems.

With the great endeavor of computer vision community, 2D human pose estimation has achieved considerable success in recent years, from the introduction of single-person pose estimation models such as the convolutional pose machine and stacked hourglass models  to multi-person pose estimation networks such as OpenPose.

Optical fiber communication systems provide the infrastructure for high-capacity data transfer. The field has shown dramatic increases in transmission capacity, and yet the demand for capacity also grows significantly. We are in a situation in which systems must continually grow in capacity to keep pace with the demand, thereby necessitating continual innovation and technical advances.

More than one million fetal deaths occur in the United States every year. Monitoring the long-term heart rate variability provides a great amount of information about the fetal health condition which requires continuous monitoring of the fetal heart rate.

Applications of semidefinite optimization in signal processing are often derived from the Kalman-Yakubovich-Popov lemma and its extensions, which give sum-of-squares theorems of nonnegative trigonometric polynomials and generalized polynomials.