scholarly journals Noise Removal Algorithm for Out-of-focus Blurred Images Based on Bilateral Filtering

2021 ◽  
Vol 15 ◽  
pp. 1314-1323
Author(s):  
Wenfang Zhang ◽  
Chi Xu
Keyword(s):  
Feature Matching ◽  
Noise Removal ◽  
Bilateral Filtering ◽  
Scene Image ◽  
Contour Feature ◽  
Grid Block ◽  
Blurred Images ◽  
Fuzzy Image ◽  
Image Detail ◽  
Feature Resolution

The feature resolution of traditional methods for fuzzy image denoising is low, for the sake of improve the strepitus removal and investigation ability of defocused blurred night images, a strepitus removal algorithm based on bilateral filtering is suggested. The method include the following steps of: Building an out-of-focus blurred night scene image acquisition model with grid block feature matching of the out-of-focus blurred night scene image; Carrying out information enhancement processing of the out-of-focus blurred night scene image by adopting a high-resolution image detail feature enhancement technology; Collecting edge contour feature quantity of the out-of-focus blurred night scene image; Carrying out grid block feature matching design of the out-of-focus blurred night scene image by adopting a bilateral filtering information reconstruction technology; And building the gray-level histogram information location model of the out-of-focus blurred night scene image. Fuzzy pixel information fusion investigation method is used to collect gray features of defocused blurred night images. According to the feature collection results, bilateral filtering algorithm is used to automatically optimize the strepitus removal of defocused blurred night images. The simulation results show that the out-of-focus blurred night scene image using this method for machine learning has better strepitus removal performance, shorter time cost and higher export peak signal-to-strepitus proportion.

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.

New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise

2013 ◽  
Vol 3 (4) ◽  
pp. 263-282 ◽  
Author(s):  
Yiqiu Dong ◽  
Tieyong Zeng
Keyword(s):  
Multiplicative Noise ◽  
Noise Removal ◽  
Variational Model ◽  
Convex Model ◽  
Split Bregman ◽  
New Approach ◽  
Uniqueness Of The Solution ◽  
Minimisation Problem ◽  
The Stability ◽  
Blurred Images

AbstractA new hybrid variational model for recovering blurred images in the presence of multiplicative noise is proposed. Inspired by previous work on multiplicative noise removal, an I-divergence technique is used to build a strictly convex model under a condition that ensures the uniqueness of the solution and the stability of the algorithm. A split-Bregman algorithm is adopted to solve the constrained minimisation problem in the new hybrid model efficiently. Numerical tests for simultaneous deblurring and denoising of the images subject to multiplicative noise are then reported. Comparison with other methods clearly demonstrates the good performance of our new approach.

A Research on the Optimization of Fuzzy Image

2013 ◽  
Vol 409-410 ◽  
pp. 1593-1596
Author(s):  
Xue Feng Wu ◽  
Yu Fan
Keyword(s):  
Mathematical Model ◽  
Image Restoration ◽  
Point Spread Function ◽  
Motion Blur ◽  
Inverse Filtering ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Blurred Images ◽  
Fuzzy Image

The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given According to the characteristics of blurred images the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration, Lucy-Richardson image restoration and Wiener image restoration. The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved, and the image restoration is more stable.

scholarly journals An Alternating Direction Method for Mixed Gaussian Plus Impulse Noise Removal

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Si Wang ◽  
Ting-Zhu Huang ◽  
Xi-le Zhao ◽  
Jun Liu
Keyword(s):  
Total Variation ◽  
Numerical Experiments ◽  
Impulse Noise ◽  
State Of The Art ◽  
Noise Removal ◽  
Alternating Direction Method ◽  
Impulse Noise Removal ◽  
Alternating Direction ◽  
Total Variation Model ◽  
Blurred Images

A combined total variation and high-order total variation model is proposed to restore blurred images corrupted by impulse noise or mixed Gaussian plus impulse noise. We attack the proposed scheme with an alternating direction method of multipliers (ADMM). Numerical experiments demonstrate the efficiency of the proposed method and the performance of the proposed method is competitive with the existing state-of-the-art methods.

Adaptive bilateral filtering for noise removal in depth upsampling

2010 ◽  
Author(s):  
Donghoon Yeo ◽  
Ehsan ul haq ◽  
Jongdae Kim ◽  
Mirza Waqar Baig ◽  
Hyunchul Shin
Keyword(s):  
Noise Removal ◽  
Bilateral Filtering

scholarly journals QualityAdaptive sharpness enhancement and noise removal of a colour images based on the bilateral filtering

2012 ◽  
pp. 49-55
Author(s):  
V. Durgarchana ◽  
B. Praveen ◽  
B. Sridhar
Keyword(s):  
Bilateral Filter ◽  
Noise Removal ◽  
Color Images ◽  
Experimental Results ◽  
Perceptual Quality ◽  
Bilateral Filtering ◽  
New Approach ◽  
Unsharp Mask ◽  
Sharpness Enhancement ◽  
Restoration Algorithms

In this paper, we present the Adaptive Bilateral Filter (ABF) for sharpness enhancement and noise removal of a colour images. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms. Compared with an USM based sharpening method, the optimal unsharp mask (OUM), In terms of noise removal, ABF will outperform the bilateral filter and the OUM. ABF works well for both gray images and color images. Due to operation of sharpening of colour images along the edge slope tend to poseterize the image using ABF by pulling up or pulling down the colour images. The proposed method is effective at removing signal noise while enhancing the experimental results in perceptual quality both quantatively and qualitatively.

scholarly journals Restoring Poissonian Images by a Combined First-Order and Second-Order Variation Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Le Jiang ◽  
Jin Huang ◽  
Xiao-Guang Lv ◽  
Jun Liu
Keyword(s):  
Lagrange Equation ◽  
Noise Removal ◽  
Poisson Noise ◽  
Second Order ◽  
Variational Model ◽  
Gradient Descent Method ◽  
First Order ◽  
Order Variation ◽  
Imaging Science ◽  
Blurred Images

The restoration of blurred images corrupted by Poisson noise is an important topic in imaging science. The problem has recently received considerable attention in recent years. In this paper, we propose a combined first-order and second-order variation model to restore blurred images corrupted by Poisson noise. Our model can substantially reduce the staircase effect, while preserving edges in the restored images, since it combines advantages of the first-order and second-order total variation. We study the issues of existence and uniqueness of a minimizer for this variational model. Moreover, we employ a gradient descent method to solve the associated Euler-Lagrange equation. Numerical results demonstrate the validity and efficiency of the proposed method for Poisson noise removal problem.

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