Subjective tests for image fusion evaluation and objective metric validation

Petrović, Vladimir

doi:10.1016/j.inffus.2005.05.001

Cited by 142 publications

(77 citation statements)

References 20 publications

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“…The subjective test database released by Petrovic [14] was selected for the metric validation. The database contained 120 pairs of registered gray image, all of which were captured in real or realistic conditions by using different sensors.…”

Section: Validation Experiments and Discussionmentioning

confidence: 99%

“…All the fused images were evaluated based on the ITU recommendation [20] and widely used. In the work of Petrovic [14], the two indicators, namely, subjective relevance (SR) and correct ranking (CR), was suggested for an objective comparison of the fusion metrics. The higher the SR and CR, the better the performance of the image fusion metric.…”

Section: Validation Experiments and Discussionmentioning

confidence: 99%

“…Section 3 describes the principle of the fusion metric based on log-Gabor filters. Section 4 compares the proposed metric with several other fusion metrics in Petrovic's database [14]. Finally, Section 5 presents the conclusions drawn.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Image Fusion Metric for Intelligent Manufacturing Information System

Yan¹,

Zheng²,

Li³

et al. 2018

dtetr

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Abstract. An image fusion metric is commonly used to evaluate a fusion scheme because subjective evaluation cannot work in an intelligent manufacturing information system. In this study, an objective image fusion metric based on a log-Gabor filter (LGIMF) is presented. This metric can be calculated in five steps: (1) filtering the source and fused images into sub-bands, (2) constructing an ideal synthesis image by applying the maximization principle from the sub-band of the source images, (3) capturing the variation information between the real fused image and the ideal synthesis image in each sub-band, (4) measuring the sub-band fusion visual information by using the signalto-noise ratio model, and (5) weighting the sub-band fusion visual information to determine the overall quality. In our experiment, the proposed fusion metric is compared with other well-known metrics by using a subjective test dataset. We found that the LGIMF was more consistent in subjective perception compared with the other metrics.

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Section: Validation Experiments and Discussionmentioning

confidence: 99%

Section: Validation Experiments and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Image Fusion Metric for Intelligent Manufacturing Information System

Yan¹,

Zheng²,

Li³

et al. 2018

dtetr

View full text Add to dashboard Cite

show abstract

“…Although he used entropy in communication, the concept may be also employed as a measure and as a way of quantifying the information content of digital images [8]. A digital image consists of pixels arranged in rows and columns with each pixel being defined by both its position and its grey scale level.…”

Section: Entropy Calculationmentioning

confidence: 99%

“…In addition, we have implemented and tested our method on other medical images with results that show an improvement over results obtained using DWT and FCT. In order to evaluate fused image quality we adopt the use of entropy (H), peak signal to noise ratio (PSNR) and root mean square error (RMSE) [8] of the fused image. Fused image quality may also be evaluated by subjective criteria for example, if the entropy and PSNR values of a fused image are high whilst the value of RMSE is low then, we could infer that the quality of the fused image will be better.…”

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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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