Image Evaluation Factors

Yao, Hongxun; Huseh, Min-Yu; Yao, Guilin; Liu, Yazhou

doi:10.1007/11559573_32

Cited by 9 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To assess the performance of every method objectively, Three indexes ,i.e., average gradient (AG) [12], information entropy (IE) [13] and edge intensity (EI) [14] are used as quality measures. Therein, the larger AG indicates the better clarity and contrast, larger IE indicates the richer information and larger EI indicates the better clarity.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Detail-Enhanced Multimodal Medical Image Fusion

Yang

Chen

Qiu

2014

2014 IEEE 17th International Conference on Computational Science and Engineering

View full text Add to dashboard Cite

Many traditional medical image fusion methods cannot well preserve details of source images in the fused image. Aiming at handling this issue, a novel medical image fusion scheme based on gain control is developed by nonsubsampled contourlet transform (NSCT). To enhance image details, the proposed method applies power law transformation to tune coefficients of each decomposed subband, and adjusts the strength of the subband signals by smooth gain control. Finally, the fused image with more details is constructed by the inverse NSCT. Experimental results demonstrate that our method can produce better fusion performance than some existing methods by the comparison of visual perception and objective quality assessment.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

Detail-Enhanced Multimodal Medical Image Fusion

Yang

Chen

Qiu

2014

2014 IEEE 17th International Conference on Computational Science and Engineering

View full text Add to dashboard Cite

show abstract

“…As for the performance measurement, no reference image quality assessment metrics, including contour-volume (CV [22]), uniform intensity distribution (UID [22]), and rootmean-square contrast (RMSC [23]), are adopted to measure the enhancement effect.…”

Section: Compared Methods and Performance Measurementmentioning

confidence: 99%

Detail Enhancement for Infrared Images Based on Propagated Image Filter

Peng

Yan

Zhao

2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

For displaying high-dynamic-range images acquired by thermal camera systems, 14-bit raw infrared data should map into 8-bit gray values. This paper presents a new method for detail enhancement of infrared images to display the image with a relatively satisfied contrast and brightness, rich detail information, and no artifacts caused by the image processing. We first adopt a propagated image filter to smooth the input image and separate the image into the base layer and the detail layer. Then, we refine the base layer by using modified histogram projection for compressing. Meanwhile, the adaptive weights derived from the layer decomposition processing are used as the strict gain control for the detail layer. The final display result is obtained by recombining the two modified layers. Experimental results on both cooled and uncooled infrared data verify that the proposed method outperforms the method based on log-power histogram modification and bilateral filter-based detail enhancement in both detail enhancement and visual effect.

show abstract

“…The paper uses the higher-order statistics of images to estimate the specific regional quality. The definition of image edge cv Ft in the literature [9] is as follows: cv 0 0…”

Section: The Assessment Of Region Image Qualitymentioning

confidence: 99%

Fusion Algorithm of Infrared and TV Image Based on Image Quality Evaluation Method

Zhou

Luo²

2014

AMM

View full text Add to dashboard Cite

The main method of Infrared and TV image fusion is based on image transform and fusion rule selection. The study implies that the information quantity of Infrared and TV image has significant effect on the result of fusion. This paper proposes one fusion algorithm of infrared and TV image based on image quality evaluation method. The basic idea of the algorithm is that the whole image or the image areas which contains better quantity of information play a leading role in the fusion process. Firstly, algorithm performs the global quality evaluation of infrared image and TV image, and then selects the corresponding fusion strategy according to the quantity of information. The source image has been divided into the high frequency coefficients and low-frequency coefficients using wavelet transform. Second, algorithm selects the rule of coefficients fusion based on the relative image quality of local regions of the image. The algorithm of this paper avoids the interference of low quality image, makes full use of the area of higher information content and achieves the favorable consistency of the fused image. Experiments demonstrate the effectiveness of the algorithm.

show abstract

Image Evaluation Factors

Cited by 9 publications

References 6 publications

Detail-Enhanced Multimodal Medical Image Fusion

Detail-Enhanced Multimodal Medical Image Fusion

Detail Enhancement for Infrared Images Based on Propagated Image Filter

Fusion Algorithm of Infrared and TV Image Based on Image Quality Evaluation Method

Contact Info

Product

Resources

About