Multi-view image refocusing

Image refocusing is a potential research area in computer graphics and computer vision. By definition it means focusing an image again, or changing the emphasized region in a given image. To achieve the focusing job, it requires shallow depth of field to create a focus-defocus scene, which depends on larger size of lens aperture. In our project, we simulate a larger camera lens aperture by using several photos taken from slightly different viewpoints. Based on these images, a layer depth map is generated to present how the objects distribute in the real world scene. User can arbitrarily select one of the objects/layers to focus, and other parts will be naturally blurred according to their depth values in the scene. This project can be divided into two parts. One is how to produce a layer depth map. Computing a depth map is actually a work of labeling assignment. This type of problem can be solved by finding a minimum value of a constructed energy function. Graph Cuts algorithm is one of the most efficient optimization methods. We use it to optimize our built energy function due to its feature of fast convergence. The second part is to blur each layer that is not assigned to be focused. Several blurry algorithms are applied to achieve this goal. In this paper, I first describe some related work and background studies on the labeling assignment theories and their related topics in vision area. I then explore the refocus-related principals in computational photography. Based on these studies, I go through our image refocusing project in details and compare the experimental results to other existed approaches. Finally, I proposed some possible future work in this research area.

Author dc:creator

• LI RUORU

• refocusing,depth of field,label assignment,computational photography,computer vision,camera aperture