A no-reference Image sharpness metric based on structural information using sparse representation

2016 ◽  
Vol 369 ◽  
pp. 334-346 ◽  
Author(s):  
Qingbo Lu ◽  
Wengang Zhou ◽  
Houqiang Li
Keyword(s):  
Sparse Representation ◽  
Structural Information ◽  
Reference Image ◽  
Image Sharpness ◽  
Sharpness Metric

scholarly journals A No-Reference Sharpness Metric Based on Structured Ringing for JPEG2000 Images

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Zhipeng Cao ◽  
Zhenzhong Wei ◽  
Guangjun Zhang
Keyword(s):  
Edge Detection ◽  
Anisotropic Diffusion ◽  
Experimental Results ◽  
Reference Image ◽  
Image Sharpness ◽  
Ringing Artifacts ◽  
Sharpness Metric ◽  
Sharpness Metrics ◽  
Degraded Images

This work presents a no-reference image sharpness metric based on human blur perception for JPEG2000 compressed image. The metric mainly uses a ringing measure. And a blurring measure is used for compensation when the blur is so severe that ringing artifacts are concealed. We used the anisotropic diffusion for the preliminary ringing map and refined it by considering the property of ringing structure. The ringing detection of the proposed metric does not depend on edge detection, which is suitable for high degraded images. The characteristics of the ringing and blurring measures are analyzed and validated theoretically and experimentally. The performance of the proposed metric is tested and compared with that of some existing JPEG2000 sharpness metrics on three widely used databases. The experimental results show that the proposed metric is accurate and reliable in predicting the sharpness of JPEG2000 images.

Fused Image Quality Measure Based on Structural Similarity

2011 ◽  
Vol 255-260 ◽  
pp. 2072-2076
Author(s):  
Yi Yong Han ◽  
Jun Ju Zhang ◽  
Ben Kang Chang ◽  
Yi Hui Yuan ◽  
Hui Xu
Keyword(s):  
Image Fusion ◽  
Structural Information ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reference Image ◽  
Human Visual Perception ◽  
Subjective Evaluations ◽  
Fused Image ◽  
Fusion Methods ◽  
Image Quality Measure

Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we present a new approach using structural similarity index for assessing quality in image fusion. The advantages of our measures are that they do not require a reference image and can be easily computed. Numerous simulations demonstrate that our measures are conform to subjective evaluations and can be able to assess different image fusion methods.

scholarly journals AUTOMATIC COREGISTRATION FOR MULTIVIEW SAR IMAGES IN URBAN AREAS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 651-654 ◽  
Author(s):  
Y. Xiang ◽  
W. Kang ◽  
F. Wang ◽  
H. You
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Structural Information ◽  
Transformation Model ◽  
Reference Image ◽  
Building Detection ◽  
Phase Congruency ◽  
Sar Images ◽  
Local Variance ◽  
Complex Structural

Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC) and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

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