Multiresolution Decomposition Schemes Using the Parameterized Logarithmic Image Processing Model with Application to Image Fusion

Abstract

New pixel-and region-based multiresolution image fusion algorithms are introduced in this paper using the Parameterized Logarithmic Image Processing (PLIP) model, a framework more suitable for processing images. A mathematical analysis shows that the Logarithmic Image Processing (LIP) model and standard mathematical operators are extreme cases of the PLIP model operators. Moreover, the PLIP model operators also have the ability to take on cases in between LIP and standard operators based on the visual requirements of the input images. PLIP-based multiresolution decomposition schemes are developed and thoroughly applied for image fusion as analysis and synthesis methods. The new decomposition schemes and fusion rules yield novel image fusion algorithms which are able to provide visually more pleasing fusion results. LIP-based multiresolution image fusion approaches are consequently formulated due to the generalized nature of the PLIP model. Computer simulations illustrate that the proposed image fusion algorithms using the Parameterized Logarithmic Laplacian Pyramid, Parameterized Logarithmic Discrete Wavelet Transform, and Parameterized Logarithmic Stationary Wavelet Transform outperform their respective traditional approaches by both qualitative and quantitative means. The algorithms were tested over a range of different image classes, including out-of-focus, medical, surveillance, and remote sensing images.

Multiscale contrast similarity deviation: An effective and efficient index for perceptual image quality assessment

“…The reason is that multiscale method incorporates image details at different resolutions, and contrast is relevant to the viewing distance. The contrast combined with multiscale representation were widely used in the literature of image enhancement [32][33][34]38,39] and image fusion [35][36][37], where the multiscale representation was implemented by pyramid decomposition [34], [36], wavelet decompositions [32,34,36], scale-space approach [33] and homomorphic transform [39]. In view of efficiency, the multiscale representation in the proposed model, however, was operated by iteratively applying a low-pass filter and downsampling the filtered image by a factor of 2, as did in MS-SSIM [4].…”

A generalized gamma correction algorithm based on the SLIP model

“…Based on a new imaging device model [28], a generalized LIP (GLIP) model has been developed [29]. Other extensions of the LIP model include the parametric [21,30], the pseudo and the harmonic LIP models [31,32], and the symmetric extension [33]. The LR model has also been recently extended from two perspectives: the Bregman divergence [34] and the triangular norm [6].…”

Pyramid-based image empirical mode decomposition for the fusion of multispectral and panchromatic images

“…Perform the spline interpolation for upper and lower envelopes u (i, j) (p g ,q g ) and 1 (i, j) (p g ,q g ). Compute the mean envelope m (i, j) (p g ,q g ) from the upper and lower envelopes, as shown in (9).…”

“…This approach is widely used in image fusion because the advantage of multi-scale analysis may improve the fusion results. The multi-scale approach includes the Wavelet transform [7], empirical mode decomposition (EMD) [8], parameterized logarithmic image processing [9], as well as other methods. The Wavelet approach transforms the original images into several high and low frequency layers before replacing the high frequency of MS image with those that are from panchromatic image.…”