Fusion of Blurred Images

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.

Download Full-text

An Iterative Method for Restoring Noisy Blurred Images.

10.21236/ada142201 ◽

1984 ◽

Cited By ~ 1

Author(s):

A. K. Katsaggelos ◽

J. Biemond ◽

R. M. Mersereau ◽

R. W. Schafer

Keyword(s):

Iterative Method ◽

Blurred Images

Download Full-text

Optical Aberration Calibration and Correction of Photographic System Based on Wavefront Coding

Sensors ◽

10.3390/s21124011 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4011

Author(s):

Chuanwei Yao ◽

Yibing Shen

Keyword(s):

Imaging System ◽

Optical Design ◽

Computational Imaging ◽

Large Field ◽

Wavefront Aberration ◽

Imaging Method ◽

Image Deconvolution ◽

Pupil Function ◽

Wavefront Coding ◽

Blurred Images

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.

Download Full-text