Visibility Improvement in WAMI

Shadow Illumination and Single Image Super Resolution for Visibility Improvement

When processing WAMI (Wide Area Motion Imagery) data several preprocessing techniques can be applied to significantly improve the visibility of the imagery. The objective of this research is to improve visibility in low/non-uniform lighting conditions for wide area surveillance applications and to enhance features to improve the performance of automatic object detection/tracking/recognition algorithms on wide area surveillance data. Although in most cases the human eye can observe the enhancement of the imagery, we demonstrate the improvement in usability of data by comparing the results of key feature points extraction methods. We analyze the improvements in the number and quality of feature points extracted before and after the visibility improvement technique is applied.

We utilize two key point extractors, Harris Corner Detection and the Speeded Up Robust Features (SURF), on the original frame and processed frame to evaluate the effects of the visibility improvement technique. A key feature point can be defined as a point where there are two dominant and different edge directions in a local neighborhood of the point. These points are used in image registration, an essential step in the wide area object tracking process. Key interest point detectors are also used in Video Stabilization, 3D Scene Creation and Object Classification.

The image on the right illustrates a cropped downsampled wide area frame take from airplane. When scrolling over the image the results of the visibility improvement technique appear. The visibility improvement technique consists of two major preprocessing steps. 1) Nonlinear Neighborhood Dependant Image Enhancement and 2) Single Image Super Resolution.