Splicing Localization in Tampered Blurred Images

Author(s):  
Seyede-Elahe Abdosalehi ◽  
Ahmad Mahmoodi-Aznaveh
Keyword(s):  
Author(s):  
Suresha .M ◽  
. Sandeep

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.


1984 ◽  
Author(s):  
A. K. Katsaggelos ◽  
J. Biemond ◽  
R. M. Mersereau ◽  
R. W. Schafer

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4011
Author(s):  
Chuanwei Yao ◽  
Yibing Shen

The image deconvolution technique can recover potential sharp images from blurred images affected by aberrations. Obtaining the point spread function (PSF) of the imaging system accurately is a prerequisite for robust deconvolution. In this paper, a computational imaging method based on wavefront coding is proposed to reconstruct the wavefront aberration of a photographic system. Firstly, a group of images affected by local aberration is obtained by applying wavefront coding on the optical system’s spectral plane. Then, the PSF is recovered accurately by pupil function synthesis, and finally, the aberration-affected images are recovered by image deconvolution. After aberration correction, the image’s coefficient of variation and mean relative deviation are improved by 60% and 30%, respectively, and the image can reach the limit of resolution of the sensor, as proved by the resolution test board. Meanwhile, the method’s robust anti-noise capability is confirmed through simulation experiments. Through the conversion of the complexity of optical design to a post-processing algorithm, this method offers an economical and efficient strategy for obtaining high-resolution and high-quality images using a simple large-field lens.


1998 ◽  
Vol 100 (1-3) ◽  
pp. 77-87 ◽  
Author(s):  
Tae Yong Kim ◽  
Joon Hee Han
Keyword(s):  

2009 ◽  
Vol 45 (3) ◽  
pp. 269-271
Author(s):  
V. R. Fazylov ◽  
N. K. Shcherbakova
Keyword(s):  

1973 ◽  
Vol 12 (7) ◽  
pp. 1713 ◽  
Author(s):  
D. P. Jablonowski ◽  
Sing H. Lee

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