Optimisation of Image Fusion using Feature Matching Based on SIFT and RANSAC

Background/Objectives: Image fusion is the technique which merges the input images to obtain the focused single image. A new method is proposed in this paper to optimise image fusion using feature matching based Scale Invariant Feature Transform (SIFT) and Random Sample Consensus (RANSAC). Methods/Analysis: In our proposed method, two input images are fused using Stationary Wavelet Transform to get a single focused image. Then, feature matching technique called SIFT is applied to match the corresponding features between two images. Further, RANSAC is applied to further optimise the result of SIFT and get a final fused image. Findings: Quantitative and visual results show that a highly focused and better fused image is obtained after feature matching with SIFT and further refinement with RANSAC. The proposed method is robust and independent of scale, light intensity, orientation of camera etc. Applications: The methodology for image fusion may be applied to stereo-images. Feature matching based on SIFT and RANSAC may be used to reconstruct a 3D view from stereo-images.