Optimal multi-focus contourlet-based image fusion algorithm selection

Abstract

Multi-focus image fusion is becoming increasingly prevalent, as there is a strong initiative to maximize visual information in a single image by fusing the salient data from multiple images for visualization. This allows an analyst to make decisions based on a larger amount of information in a more efficient manner because multiple images need not be cross-referenced. The contourlet transform has proven to be an effective multi-resolution transform for both denoising and image fusion through its ability to pick up the directional and anisotropic properties while being designed to decompose the discrete two-dimensional domain. Many studies have been done to develop and validate algorithms for wavelet image fusion, but the contourlet has not been as thoroughly studied. When the contourlet coefficients for the wavelet coefficients are substituted in image fusion algorithms, it is contourlet image fusion. There are a multitude of methods for fusing these coefficients together and the results demonstrate that there is an opportunity for fusing coefficients together in the contourlet domain for multi-focus images. This paper compared the algorithms with a variety of no reference image fusion metrics including information theory based, image feature based and structural similarity based assessments to select the image fusion method.

Robust sparse representation based multi-focus image fusion with dictionary construction and local spatial consistency

“…wherein multi-scale transform-based (MST-based) image fusion algorithms are one of the most popular choices [15]. Various MST-based fusion methods have been discussed over the years, ranging from the early wavelet [16] and pyramid [17] transforms to the recently developed multi-scale geometric analysis approaches, such as curvelet [18], contourlet [19], and shearlet [20].…”

Image Registration and Fusion of Visible and Infrared Integrated Camera for Medium-Altitude Unmanned Aerial Vehicle Remote Sensing

“…In the transform domain fusion, the images should be transformed into the transform domain space before the fusion of the coefficients is conducted. This type of methods mainly include the Laplace pyramid transform-based method [43], wavelet transform-based method [44], ridgelet transform-based method [45], contourlet transform-based method [46], NSCT-based method [47], compressed sensing-based method [48], and sparse representation-based method [49].…”

DCNN Optimization using Wavelet-based Image Fusion

“…One possible solution to this is with the use of image fusion techniques, in which images of the same scene captured from different angles, or with multi-modal or multispectral sensors are fused into a single stream of data which can be used as a model's input [4,5,6,7]. The development of these multisensory applications began in the 1980's as subcategory of remote sensing.…”