Multifocus Image Fusion Based on Multiresolution Pyramid and Bilateral Filter

摘要

ABSTRACT Multifocus image fusion is the process for enhancing the perception of a vision by combining substantial information captured by the camera from different focus points. It is very difficult to obtain a finer focus for all objects in the scene. The solution to the problem is to capture several multifocus images with different focal points, which are merged to obtain a single fused image with improved Depth of Field (DOF). The proposed method utilizes the unique significant texture features that are obtained from the Bilateral Filter (BLT). These unique features are used to compute weight map function that controls the transformation of details from multifocus source images to a single fused image. A pyramid-based multiresolution decomposition is proposed for analyzing spatial frequency components and then the fused image is reconstructed from modified spatially band-passed images by applying an inverse multiresolution transform. The fused image yields better results in terms of entropy, mean, standard deviation, average gradient, and PSNR values over existing state-of-the-art multifocus image fusion techniques. The proposed algorithm retains the actual shape of objects and also avoids the introduction of visible artifacts in the fused image.