Based on our recent successful experiences in the study of image denoising and image segmentation, in this three-year project, we are going to explore adaptive variational models and to develop efficient numerical methods for some fundamental issues in the image processing. We will mainly focus on the following three image processing tasks which are frequently arising from numerous applications: contrast enhancement, fusion, and super-resolution reconstruction. (1) Image contrast enhancement: We will study an adaptive variational model for contrast enhancement of low-light images, in which the gradient of the desired new image is close to that of given image but with reduced variance to balance inhomogeneous illumination. We will show that this adaptive variational model can be solved efficiently by using the split Bregman iterative scheme. (2) Image fusion: The main goal of image fusion is to integrate several sources images of the same scene into a more informative image. We will study an adaptive variational image fusion model based on the first and second-order gradient information. The first task is to explore a feature selection strategy which is then integrated into the adaptive variational model for image fusion. (3) Image super-resolution reconstruction: The objective of super-resolution reconstruction is to take a set of one or more low-resolution input images of a scene to increase the spatial resolution without a loss in signal-to-noise ratio. Combining with the image inpainting techniques, we will minimize an adaptive energy functional to reconstruct a higher-resolution image.
|Effective start/end date||1/08/21 → 31/07/22|
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):