Applications of edge preservation ratio in image processing

Edge preservation ratio (EPR) is a full-reference metric for objective image quality assessment (IQA). It is under the assumption that key messages to human visual systems are mainly from image structures, and these structures can be extracted by edge detection. EPR measure is twofold: accuracy and robustness, and a color map is synthesized to reveal structure changes before and after image processing. The feasibility and superiority of EPR have been validated via image magnification and noise reduction. Experimental results suggest that: (1) it is challenging to fully recover lost messages by image magnification; (2) high image contrast may be derived from concise and distinct image structures.

[1]  Yaoqin Xie,et al.  Interactive Medical Image Segmentation Using Snake and Multiscale Curve Editing , 2013, Comput. Math. Methods Medicine.

[2]  Zhou Wang,et al.  Why is image quality assessment so difficult? , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Ardeshir Goshtasby,et al.  On the Canny edge detector , 2001, Pattern Recognit..

[4]  Wufan Chen,et al.  MTV: modified total variation model for image noise removal , 2011 .

[5]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[6]  Jorge Herbert de Lira,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[7]  L. Xing,et al.  Feature-based rectal contour propagation from planning CT to cone beam CT. , 2008, Medical physics.

[8]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[9]  Yaoqin Xie,et al.  Nonrigid Registration of Lung CT Images Based on Tissue Features , 2013, Comput. Math. Methods Medicine.

[10]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Lei Zhang,et al.  An edge-guided image interpolation algorithm via directional filtering and data fusion , 2006, IEEE Transactions on Image Processing.

[12]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[14]  Lei Zhang,et al.  Canny edge detection enhancement by scale multiplication , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Nicola Asuni,et al.  Submitted to Ieee Transactions on Image Processing 1 Real Time Artifact-free Image Upscaling , 2022 .

[16]  Yaoqin Xie,et al.  Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology , 2013, 2013 IEEE International Conference on Information and Automation (ICIA).

[17]  Yaoqin Xie,et al.  An edge-directed interpolation method for fetal spine MR images , 2013, Biomedical engineering online.

[18]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Fatih Murat Porikli Accurate detection of edge orientation for color and multi-spectral imagery , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[20]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[21]  Weiming Dong,et al.  Image zooming using directional cubic convolution interpolation , 2012 .

[22]  Yaoqin Xie,et al.  Performance evaluation of edge-directed interpolation methods for noise-free images , 2013, ICIMCS '13.

[23]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[24]  Yaoqin Xie,et al.  Deformable image registration of liver with consideration of lung sliding motion. , 2011, Medical physics.

[25]  Sean Dougherty,et al.  Edge detector evaluation using empirical ROC curves , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[26]  Yaoqin Xie,et al.  Segmentation of abdomen MR images using kernel graph cuts with shape priors , 2013, BioMedical Engineering OnLine.