Abstract
This paper describes two effective techniques for reducing most of the static types of blur effects in remote sensed images. The first method is based on the sub-pixel shifted multi-observation of a scene. It attempts to sharp the image by down-sampling and matching a set of sub-pixel shifted frames, and calculating the statistical weighted average within the correspondent aligned pixels of the multi-frame set. The second technique reduces the blur of a single frame image by re-assigning the maximum and minimum intensity values of the blur-width area to the correspondent pixels in the direction of the gradient of the blur. Then, the area is shifted 1 pixel and the same process is done again, up to cover the full image.
Real patterns are used for the analysis. The effectiveness, adaptability and simplicity of the methods presented here, is demonstrated.
