Dynamic Image Fusion Performance Evaluation

Petrovic, V.; Cootes, Timothy F.; Pavlović, R.

doi:10.1109/icif.2007.4408120

Cited by 12 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For more accurate comparison, besides the visual analysis, the performance of fusion algorithms needs to be further measured by objective quantitative analysis tools. Here two metric tools were used: the spatial-temporal gradient preservation based video fusion performance metric (DQ AB/F ) [21] and the mutual information of IFD images (IFD MI) [9]. DQ AB/F indicates how much the spatialtemporal information is extracted and transferred into the fused image sequence.…”

Section: Fusion Results Of Clear Image Sequencesmentioning

confidence: 99%

Image Sequence Fusion and Denoising Based on 3D Shearlet Transform

Zhang

2014

Journal of Applied Mathematics

View full text Add to dashboard Cite

We propose a novel algorithm for image sequence fusion and denoising simultaneously in 3D shearlet transform domain. In general, the most existing image fusion methods only consider combining the important information of source images and do not deal with the artifacts. If source images contain noises, the noises may be also transferred into the fusion image together with useful pixels. In 3D shearlet transform domain, we propose that the recursive filter is first performed on the high-pass subbands to obtain the denoised high-pass coefficients. The high-pass subbands are then combined to employ the fusion rule of the selecting maximum based on 3D pulse coupled neural network (PCNN), and the low-pass subband is fused to use the fusion rule of the weighted sum. Experimental results demonstrate that the proposed algorithm yields the encouraging effects.

show abstract

Section: Fusion Results Of Clear Image Sequencesmentioning

confidence: 99%

Image Sequence Fusion and Denoising Based on 3D Shearlet Transform

Zhang

2014

Journal of Applied Mathematics

View full text Add to dashboard Cite

show abstract

“…Comparing this approach to the simple select-max applied to central frame only, using the objective DQ video fusion performance measure [24] on a representative sequence illustrated in Figure 3, Figure 6 below, we see that the proposed approach improves fusion performance. However, although the increase in DQ is significant, there are still large oscillations through the frames.…”

Section: Dynamic Laplacian Rolling-pyramid Fusionmentioning

confidence: 98%

“…Further to these classic algorithms, new multi-scale techniques have more recently been proposed based on the static fusion using Curvelets [50], Ridgelets [51], Contourlets [14], Shearlet [3] as well as the Dual tree complex wavelet transform (DTCWT) [48]. The static fusion methods for video fusion are generally less computationally demanding, but since they ignore the temporally varying component of the available scene information, they can result in temporally unstable fused sequences exhibiting blinking effect distortions that affect the perceived fused video quality [15,24].…”

Section: Video Fusionmentioning

confidence: 99%

“…An additional requirement, specific to video fusion is the temporal stability of the fused result, which means a temporally consistent fused output despite the dynamically changing scene content. Finally, video fusion algorithms are generally supposed to work in real-time, which means a fusion rate of at least 25 frames per second, or indeed up to 60 for real-time head-up-display applications [6,24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rolling 3D Laplacian Pyramid Video Fusion

Pavlović

Petrović

2019

Electronics

View full text Add to dashboard Cite

In this paper, we present a novel algorithm for video fusion of multi-sensor sequences applicable to real-time night vision systems. We employ the Laplacian pyramid fusion of a block of successive frames to add temporal robustness to the fused result. For the fusion rule, we first group high and low frequency levels of the decomposed frames in the block from both input sensor sequences. Then, we define local space-time energy measure to guide the selection based fusion process in a manner that achieves spatio-temporal stability. We demonstrate our approach on several well-known multi-sensor video fusion examples with varying contents and target appearance and show its advantage over conventional video fusion approaches. Computational complexity of the proposed methods is kept low by the use of simple linear filtering that can be easily parallelised for implementation on general-purpose graphics processing units (GPUs).

show abstract

“…Because subjective evaluation requires lots of time, effort, and physical & human resources, we confine our comparison to objective evaluation using established objective measures. The measure are Mutual Information (MI) [14], Xydas & Petrovic (Q AB/F ) [15], and Piella & Heijmans (Q w ) [16] metrics. These measures, although not perfect, provide a good insight into the quality of fused images.…”

Section: Performance Evaluationmentioning

confidence: 99%