Kernel-based adaptive image sampling

Abstract

This paper presents an adaptive progressive image acquisition algorithm based on the concept of kernel construction. The algorithm takes the conventional route of blind progressive sampling to sample and reconstruct the ground truth image in an iterative manner. During each iteration, an equivalent kernel is built for each unsampled pixel to capture the spatial structure of its local neighborhood. The kernel is normalized by the estimated sample strength in the local area and used as the projection of the influence of this unsampled pixel to the consequent sampling procedure. The sampling priority of a candidate unsampled pixel is the sum of such projections from other unsampled pixels in the local area. Pixel locations with the highest priority are sampled in the next iteration. The algorithm does not require to pre-process or compress the ground truth image and therefore can be used in various situations where such procedure is not possible. The experiments show that the proposed algorithm is able to capture the local structure of images to achieve a better reconstruction quality than that of the existing methods.

DOI: 10.5220/0004653100250032

7 Figures and Tables

Cite this paper

@article{Liu2014KernelbasedAI, title={Kernel-based adaptive image sampling}, author={Jianxiong Liu and Christos-Savvas Bouganis and Peter Y. K. Cheung}, journal={2014 International Conference on Computer Vision Theory and Applications (VISAPP)}, year={2014}, volume={1}, pages={25-32} }