An Efficient Multifocus Image Fusion method using Curvelet Transform and Normalization

Abstract

Multi-focus imaging fusion is a technique that puts together a fully focused object from the partly focused regions of several objects from the same scene. For producing a high quality fused image, negligible aliasing and ability to separate positive from negative frequencies characteristics are important. The ringed artifacts, however, were inserted into a fused image because of a lack of negligible aliasing and ability to separate positive from negative frequencies properties. A multifocus image fusion algorithm is proposed to resolve these issues, in conjunction with curvelet transform and normalization. First, the source images are translated to the curvelet transform. It helps in the obtaining of the curvelet frequency components. Then the frequency components are combined with a fusion rule to transform the origin frames. curvelet transform has demonstrated that it provides an effective transformation for multi-resolution imaging fusion with its negligible aliasing and ability to separate positive from negative frequencies characteristics. In order to enlarge the effectiveness of the curvelet transform based method, the normalization technique is used. The proposed fusion approach has been tested on a numeral of multifocus images and compared to various popular methods of imaging fusion. The experimental results indicate that in subjective performance and objective assessment, the proposed fusion approach could deliver better fusion results.