Objective image fusion performance characterisation

Petrovic, V.; Xydeas, C.S.

doi:10.1109/iccv.2005.175

Cited by 113 publications

(40 citation statements)

References 11 publications

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“…In mosaicing all images come from the same sensor and all images should provide the same information from a same physical target. It is still interesting to view the paper by Petrović and Xydeas [8]. They propose an objective image fusion performance metric.…”

Section: Related Workmentioning

confidence: 99%

Image Based Quantitative Mosaic Evaluation with Artificial Video

2009

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Abstract. Interest towards image mosaicing has existed since the dawn of photography. Many automatic digital mosaicing methods have been developed, but unfortunately their evaluation has been only qualitative. Lack of generally approved measures and standard test data sets impedes comparison of the works by different research groups. For scientific evaluation, mosaic quality should be quantitatively measured, and standard protocols established. In this paper the authors propose a method for creating artificial video images with virtual camera parameters and properties for testing mosaicing performance. Important evaluation issues are addressed, especially mosaic coverage. The authors present a measuring method for evaluating mosaicing performance of different algorithms, and showcase it with the root-mean-squared error. Three artificial test videos are presented, ran through real-time mosaicing method as an example, and published in the Web to facilitate future performance comparisons.

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Section: Related Workmentioning

confidence: 99%

Image Based Quantitative Mosaic Evaluation with Artificial Video

2009

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“…It evaluates the total information transferred fro m the source images to fused image [29]. Mathematically, it is defined as: , N XY/f and Nm XY/f are used to compute the total loss of informat ion and noise or artifacts in fused image which are given in [9,24,29].…”

Section: Q Xy/fmentioning

confidence: 99%

“…Mathematically, it is defined as: , N XY/f and Nm XY/f are used to compute the total loss of informat ion and noise or artifacts in fused image which are given in [9,24,29]. …”

Section: Q Xy/fmentioning

confidence: 99%

Discrete Wavelet Transform and Cross Bilateral Filter based Image Fusion

Sonam¹,

Kumar²

2017

IJISA

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Abstract-The main object ive of image fusion is to obtain an enhanced image with more relevant informat ion by integrating complimentary information fro m two source images. In this paper, a novel image fusion algorith m based on discrete wavelet transform (DWT) and cross bilateral filter (CBF) is proposed. In the proposed framewo rk, source images are decomposed into low and high frequency subbands using DWT. The low frequency subbands of the transformed images are combined using pixel averag ing method. Meanwhile, the high frequency subbands of the transformed images are fused with weighted average fusion ru le where, the weights are co mputed using CBF on both the images . Finally, to reconstruct the fused image inverse DWT is performed over the fused coefficients. The proposed method has been extensively tested on several pairs of mu lti-focus and multisensor images. To compare the results of proposed method with d ifferent existing methods, a variety of image fusion quality metrics are emp loyed for the qualitative measurement. The analysis of co mparison results demonstrates that the proposed method exhibits better results than many other fusion methods, qualitatively as well as quantitatively.

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“…With respect to the equation above, MSE corresponds to the mean square error and L is the number of grey levels in the image [6].…”

Section: Peak Signal To Noise Ratiomentioning

confidence: 99%

An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

Sumathi

Hemalatha

2011

Open Computer Science

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Image fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.Keywords: region based energy method • discrete wavelet transform • discrete fast curvelet transform • root mean square error © Versita sp. z o.o.

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Objective image fusion performance characterisation

Cited by 113 publications

References 11 publications

Image Based Quantitative Mosaic Evaluation with Artificial Video

Image Based Quantitative Mosaic Evaluation with Artificial Video

Discrete Wavelet Transform and Cross Bilateral Filter based Image Fusion

An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

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