Euclidian Norm Based Fusion Strategy for Multi Focus Images

Collecting salient and relevant information from many images and merging this to generate a quality image is the main goal of image fusion technique. Because of the camera's characteristics while photographing a scene, multi focus images will be produced. Each image of the scene has a different set of features and the merging leads to a good capture of the scene. Activity level measurement and fusion strategy are the critical areas of study in multi focus fusion. To find various focused information in transformed and spatial domains, there have been a lot of algorithms developed. Convolutional neural networks are excellent at representing deep features in an easier format and this property is used to represent multi focus images. Each pixel's activity map is used as a parameter in the fusion strategy. Euclidian norm are a good tool to find the similarities between a set of values. ℓ2 Euclidian norm along with activity map performs the fusion of feature maps collected by residual network. When compared to other fusion algorithms, the presented technique is efficient and improves the image quality. The merged images correlate with human visual perception. The algorithm is suitable for applications like remote sensing, surveillance, and medical diagnosis, etc.