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This thesis describes and evaluates a number of algorithms for reducing fixed pattern noise in image sequences. Fixed pattern noise is the dominantnoise component for many infrared detector systems, perceived as a superimposed pattern that is approximately constant for all image frames. Primarily, methods based on estimation of the movement between individual image frames are studied. Using scene-matching techniques, global motion between frames can be successfully registered with sub-pixel accuracy. This allows each scene pixel to be traced along a path of individual detector elements. Assuming a static scene, differences in pixel intensities are caused by fixed pattern noise that can be estimated and removed. The algorithms have been tested by using real image data from existing infrared imaging systems with good results. The tests include both a two-dimensional focal plane array detector and a linear scanning one-dimensional detector, in different scene conditions.

This thesis describes and evaluates a number of algorithms for reducing fixed pattern noise in image sequences. Fixed pattern noise is the dominantnoise component for many infrared detector systems, perceived as a superimposed pattern that is approximately constant for all image frames. Primarily, methods based on estimation of the movement between individual image frames are studied. Using scene-matching techniques, global motion between frames can be successfully registered with sub-pixel accuracy. This allows each scene pixel to be traced along a path of individual detector elements. Assuming a static scene, differences in pixel intensities are caused by fixed pattern noise that can be estimated and removed. The algorithms have been tested by using real image data from existing infrared imaging systems with good results. The tests include both a two-dimensional focal plane array detector and a linear scanning one-dimensional detector, in different scene conditions.