Multi-focus thermal image fusion

Steerable local frequency based multispectral multifocus image fusion

“…Since our fusion method is essentially based on interest point detection, we also compare our method with best performing Fast Hessian (FH) based fusion scheme from the same category [9]. For thermal fusion, we choose a recently reported EOL based method [19] for comparison. In addition, we also compare with FH based fusion scheme.…”

“…In [17], Li et al has evaluated the performance of such multiresolution transforms for multifocus image fusion in the visual spectrum only. Benes et al [19] proposed a new multifocus image fusion algorithm for thermal images where they employed pixel level weighted averaging based on modified EOL. But such a linear combination often fails to preserve the original information in source images leading to a degradation in the fusion performance.…”

“…4. For the thermal spectrum, we experiment with the reduced set of multifocus thermal image datasets developed by Benes et al [19]. The original thermal image database consists of five multifocus image sets with 96 images in each set.…”

“…All the sets contain a scene image with two objects but with different backgrounds, varying temperatures and different object distances. A reduced set of 10 images for each dataset is derived from the original pool of 96 images using EOL based activity level measurement [19]. The reduced image sets for the mobileinterface and the two bulbs are shown in Figs.…”

“…Three metrics RMSE (Root Mean Square Error), MAE (Mean Absolute Error) and CC (Cross Correlation) as in [19], are employed to evaluate the performance of the proposed fusion method in thermal spectrum and are described below.…”