Restoration of noisy blurred images by a smoothing spline filter

1977 ◽  
Vol 16 (12) ◽  
pp. 3147 ◽  
Author(s):  
M. J. Peyrovian ◽  
A. A. Sawchuk
Keyword(s):  
Smoothing Spline ◽  
Spline Filter ◽  
Blurred Images

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
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.

An Iterative Method for Restoring Noisy Blurred Images.

1984 ◽  
Author(s):  
A. K. Katsaggelos ◽  
J. Biemond ◽  
R. M. Mersereau ◽  
R. W. Schafer
Keyword(s):  
Iterative Method ◽  
Blurred Images

Pipelined nonlinear spline filter for speech prediction

2021 ◽  
Vol 179 ◽  
pp. 108057
Author(s):  
Zhao Zhang ◽  
Sheng Zhang ◽  
Jiashu Zhang
Keyword(s):  
Spline Filter

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

Sensors ◽  
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.

An improved calibration curve between soil pH measured in waterand CaCl2

Soil Research ◽  
2002 ◽  
Vol 40 (8) ◽  
pp. 1399 ◽  
Author(s):  
B. L. Henderson ◽  
E. N. Bui
Keyword(s):  
Statistical Analysis ◽  
Water Resources ◽  
Soil Ph ◽  
Calibration Curve ◽  
Smooth Function ◽  
Degrees Of Freedom ◽  
Additive Model ◽  
Smoothing Spline ◽  
Look Up Table ◽  
6 Degrees Of Freedom

A new pH water to pH CaCl2 calibration curve was derived from data pooled from 2 National Land and Water Resources Audit projects. A total of 70465 observations with both pH in water and pH in CaCl2 were available for statistical analysis. An additive model for pH in CaCl2 was fitted from a smooth function of pH in water created by a smoothing spline with 6 degrees of freedom. This model appeared stable outside the range of the data and performed well (R2 = 96.2, s = 0.24). The additive model for conversion of pHw to pHCa is sigmoidal over the range of pH 2.5 to 10.5 and is similar in shape to earlier models. Using this new model, a look-up table for converting pHw to pHCa was created.

Edge representation with fuzzy sets in blurred images

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