An overview of PDE based approach to image restoration

Abstract: Many digital image processing applications rely on the quality of the obtained images. Unfortunately, in the real scenario, due to the natural barriers of acquiring equipment or the presence of intermittent fluctuations in the medium, the images acquired by a scanner or digital camera are generally contaminated by different types of noises such as Gaussian noise, Speckle noise, Poisson noise, etc. Hence, image restoration is crucial for high-level image analysis, e.g., image segmentation, object recognition, scene understanding, etc. It has been observed that the goal of noise reduction with feature preservation and applicability to the multiple acquisition systems is challenging to achieve through a single method. Therefore, the accurate reconstruction of crucial features in an image from the noisy measurement is the main objective of our study.

In this seminar, I focus my interest only on the overview of partial differential equations (PDE/PDEs) based image restoration. I start this talk with a brief motivation for image restoration. Then discuss the motivation behind PDE-based image restoration, the relation between PDE and image restoration, and an overview of the existing literature. Next, I will represent some computational results for image restoration using a few existing PDE-based techniques. Finally, I will explain the objective of our present research work.

To receive the Zoom link, please contact: Niklas Lundström