Multidimensional filtering approaches for pre-processing thermal images

Abstract

Demand for sharpened thermal images drives research into pre-processing techniques. This paper describes two fast multi-frame image-processing techniques for reducing noise and some blurring effects that are typically exhibited in thermal images. The first technique cleans the thermal image from random and fixed-pattern noises. The random noise is considerably reduced by the simple principle of averaging corresponding pixels of a multi-frame sequence. For eliminating fixed-noise like effects, the technique performs, at first, conventional arithmetic mean filters within each local region of the noise pattern. Then, weighted versions of these values are subtracted from the corrupted image. The second technique attempts to recover the information hidden at a sub-pixel level. It sharpens the previously processed thermal image by down-sampling and matching a set of sub-pixel shifted frames, and finally calculating the statistical weighted average within the correspondent aligned pixels of the multi-frame set. Some variants that combine it with conventional filters are also presented. This technique effectively corrects some blurring effects typically found in thermal infrared images. For the case of a single frame image determines the direction and width of the blur slope and re-assigns the max and min values to the correspondent pixels in the gradient direction. Then, the area is shifted and the same process is done again, up to cover the full image. Image evaluation methods demonstrate the accuracy and quality of the results. In addition to reducing the hardware requirements of present designs, these algorithms increase the utility of present sensors.