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Comparison of image processing techniques is critically important in deciding which algorithm, method, or metric to use for enhanced image assessment. Image fusion is a popular choice for various image enhancement applications such as overlay of two image products, refinement of image resolutions for alignment, and image combination for feature extraction and target recognition. Since image fusion is used in many geospatial and night vision applications, it is important to understand these techniques and provide a comparative study of the methods. In this paper, we conduct a comparative study on 12 selected image fusion metrics over six multiresolution image fusion algorithms for two different fusion schemes and input images with distortion. The analysis can be applied to different image combination algorithms, image processing methods, and over a different choice of metrics that are of use to an image processing expert. The paper relates the results to an image quality measurement based on power spectrum and correlation analysis and serves as a summary of many contemporary techniques for objective assessment of image fusion algorithms.

This paper proposes an algorithm that detects the occurrence of planar homographies in an uncalibrated image pair. It then shows that this plane identi cation method c an be u s e d, to great advantage, as a rst step in an image analysis process, when point matching between images is unreliable. The detection is performed using a RANSAC-like scheme based on the linear computation of the homography matrix elements using four points. Results are shown on real image pairs where series of matched p oints belonging to common planes are automatically and correctly identi ed.

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