Block-based image fusion using multi-scale analysis to enhance depth of field and dynamic range

A novel technique for integrating information by exploring multi-scale positions with block-based fusion and to address blocking effects is discussed in the present manuscript. The source images are split into local and global layers using neighbor distance filter by extracting information at multi-scale positions. Recombined local and global layers are constructed using block-based and weighted average methods, respectively. The spatial frequency as well as exposedness factor is used to find the texture information and exposure level for respective blocks. Resulting local and global layers are then fused to generate final fused image. The method is applicable to any number of source images. Extensive experimental results are provided to show the effectiveness of proposed technique.