Image Processing Reference
In-Depth Information
suitable for shape extraction of the objects in the fused image due to their property
of removing the image details without adding any gray level bias.
Burt and Kolczynski [20] have proposed a generalized multisensor fusion using
the gradient pyramid where the process of generation of the image pyramid has
been referred to as the
pyramid transform
. Using the basis functions of gradient-of-
Gaussian pattern, they apply the pyramid transform to both of the input images. Each
of these basis functions is derived from the single prototype function via shifting,
scaling, and rotation. This process decomposes the image up to three levels of suc-
cessive approximations along four orientations. To combine the information across
multiple decompositions, authors define two fusion rules. At the locations where the
source images are similar, the fusion is achieved by averaging two images, while if
the images are significantly different, the fusion rule selects the feature pattern with
maximum saliency and copies it for the fused image.
Liu et al. have demonstrated the use of a steerable pyramid for fusion of remote
sensing images [107]. The steerable pyramid is a multi-scale and multi-orientation
decomposition with translation and rotation invariant sub-bands [167]. The low
frequency or the coarsest approximation is fused based on the magnitude of the
images at the corresponding locations. The fusion rule for the high frequency details
is derived from the strength of each of the orientations which gives the directional
information at that location. Authors refer to this selection rule as the absolute value
maximum selection (AVMS).
Piella [135] has proposed a region-based technique in a multi-resolution frame-
work as an extension of the pixel-based technique. This work provides a generalized
structure for multi-resolution fusion techniques. The input images are first segmented
which is a preparatory step toward the actual fusion. The author uses the term
activity
measure
which captures the saliency in the image. The other quantity is the match
measure which quantifies the similarity between the corresponding coefficients of
the transformed images. This structure encompasses most of the pixel-based and
region-based multi-resolution techniques, and also can be considered as the basis for
the development of new ones.
For an efficient fusion, one needs to extract the salient features from multi-scale
image decompositions. The wavelet transform has proved to be a highly popular
tool for fusion. We assume that the readers are familiar with the fundamentals of the
wavelet transform. For details on wavelets, one may refer to the dedicated texts by
Daubechies [46], Vaidyanathan [178], Mallat [111], etc. The advantages offered by
wavelets along with their theoretical background can also be found in [5, 124].
Li et al. have proposed a discrete wavelet transform (DWT)-based technique for
fusion as wavelets offer distinct advantages such as orthogonality, compactness,
and directional information [104]. Their technique is claimed to be superior to the
Laplacian pyramid-based techniques as it does not produce any visible artifacts in the
fused image as opposed to the later. Similar to the multi-resolution fusion approaches
discussed earlier, the basic principle behind the wavelet-based fusion techniques is as
follows—the set of input images is first decomposed into different multi-resolution
coefficients that preserve image information. These coefficients are appropriately
combined at each level to obtain new coefficients of the resultant image. This image is
