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Sequential Far Infrared Image Mosaic Using Coarse-to-Fine Scheme

Sequential far infrared (FIR) image mosaic is a challenging task due to repeated patterns, low contrast, image blurring, illumination change, and sparse structure. In this paper, a novel matching approach is proposed for the sequential FIR image registration, which is based on coarse-to-fine strategy and grid-based motion statistics (GMS). In the coarse matching step, a transitive relation is deduced to obtain the coordinate relations of the current registering image and its adjacent images to ensure enough number of feature points and guarantee the accuracy of the preregistration results. In the fine matching step, a principle for fine-tuning registration and a strategy for changing the distribution of the feature points are developed for finding most appropriate feature points. Based on the point pairs, the position of the current registering image can be accurately located. Besides, an effective post-processing, such as gray-scale non-uniformity correction and mosaic seam elimination, is also utilized to refine the fine-tuning results to achieve the promising mosaic effect. A comparative study and quantitative evaluation with commercial software or representative image mosaic algorithm demonstrate that better quality results can be obtained by using the proposed method.