scholarly journals New Cepstrum Based Image Restoration Algorithm for Grayscale Images

2019 ◽  
Vol 8 (11) ◽  
pp. 1920-1928
Keyword(s):  
Image Restoration ◽  
Heuristic Algorithms ◽  
Bat Algorithm ◽  
Motion Blur ◽  
Hybrid Technique ◽  
Restoration Algorithm ◽  
Blur Estimation ◽  
Blurred Images ◽  
Selection Of ◽  
Grayscale Images

Optimization is the process that relates to finding the most excellent ways for all possible solutions. From last 2-3 decades, natural algorithms play an important role in improving solutions of various problems. By comparing various meta-heuristic algorithms, researchers can make a choice to the best selection of the meta-heuristic algorithms for the proposed problem. In this particular research, we have applied New Cepstrum based technique of image restoration to find out PSF parameters of motion blurred images as a primary technique. In addition, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), BAT Algorithm and GA-BAT hybrid technique etc. are also applied to optimize the blur parameters for calculated by new cepstrum based technique for blur estimation. This aids in analyzing the performance of each algorithm on the same primary technique. The performance analysis of all four algorithms aid in making the decision on the best meta-heuristic algorithm of the cepstrum based technique and to identify the preciseness of the motion blur. All four methods are applied to the same set of images. The algorithm is tested and compared using grayscale images and the benchmarking freely available online datasets, respectivel

scholarly journals Learning to Deblur Face Images via Sketch Synthesis

2020 ◽  
Vol 34 (07) ◽  
pp. 11523-11530 ◽  
Author(s):  
Songnan Lin ◽  
Jiawei Zhang ◽  
Jinshan Pan ◽  
Yicun Liu ◽  
Yongtian Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Domain Knowledge ◽  
Motion Blur ◽  
Deep Convolutional Neural Networks ◽  
Face Images ◽  
Restoration Algorithm ◽  
Deep Cnn ◽  
Blur Estimation ◽  
Face Deblurring

The success of existing face deblurring methods based on deep neural networks is mainly due to the large model capacity. Few algorithms have been specially designed according to the domain knowledge of face images and the physical properties of the deblurring process. In this paper, we propose an effective face deblurring algorithm based on deep convolutional neural networks (CNNs). Motivated by the conventional deblurring process which usually involves the motion blur estimation and the latent clear image restoration, the proposed algorithm first estimates motion blur by a deep CNN and then restores latent clear images with the estimated motion blur. However, estimating motion blur from blurry face images is difficult as the textures of the blurry face images are scarce. As most face images share some common global structures which can be modeled well by sketch information, we propose to learn face sketches by a deep CNN so that the sketches can help the motion blur estimation. With the estimated motion blur, we then develop an effective latent image restoration algorithm based on a deep CNN. Although involving the several components, the proposed algorithm is trained in an end-to-end fashion. We analyze the effectiveness of each component on face image deblurring and show that the proposed algorithm is able to deblur face images with favorable performance against state-of-the-art methods.

Fast Restoration Algorithm for Rotational Motion Blurred Images

2011 ◽  
Vol 267 ◽  
pp. 969-973
Author(s):  
Jian Yang Zhou ◽  
Yin Tao Yang ◽  
Yi Liang Wu
Keyword(s):  
Image Restoration ◽  
Rotational Motion ◽  
Fast Algorithm ◽  
Degradation Model ◽  
Experimental Platform ◽  
Restoration Algorithm ◽  
Blurred Image ◽  
Comparison And Analysis ◽  
Blurred Images ◽  
Image Degradation Model

This paper first introduces the history and research status of motion-blurred image restoration, and then establishes a rotating image degradation model. we present a fast algorithm for real-time hardware restoration of rotating motion blurred images, the algorithm is simulated and experimental platform is built for the actual verification. Through comparison and analysis of the recovery results, we verify the accuracy of the algorithm, and it shows that this rotational motion blurred image restoration algorithm can receive satisfactory results.

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.

Research on Cross-Correlative Blur Length Estimation Algorithm in Motion Blur Image

2016 ◽  
Vol 20 (1) ◽  
pp. 155-162 ◽  
Author(s):  
Li Dongming ◽  
◽  
Su Zhengbo ◽  
Su Wei ◽  
Zhang Lijuan ◽  
...  
Keyword(s):  
Estimation Method ◽  
Computation Time ◽  
Estimation Algorithm ◽  
Motion Blur ◽  
Transform Method ◽  
Length Estimation ◽  
Restoration Algorithm ◽  
Degraded Image ◽  
Frame Motion ◽  
Blurred Images

This paper proposes a motion blur length estimation method that is applied to motion blur image restoration. This method applies a cross-correlation algorithm to multi-frame motion-degraded images. In order to find the motion blur parameters, the Radon transform method is used to estimate the motion blur angle. We extract the gray value of pixels around the blur center, calculate the correlation for obtaining motion blur length, and use the Lucy-Richardson iterative algorithm to restore the degraded image. Experiment results show that this method can accurately estimate blur parameters, reduce noise, and obtain better restoration results. The method achieves good results on artificially blurred images and natural images (by the camera shake). The advantage of our algorithm that uses the Lucy-Richardson restoration algorithm compared with the Wiener filtering algorithm is made obvious with less computation time and better restored effects.

Image Restoration with Lucy-Richardson Algorithm

2013 ◽  
Vol 462-463 ◽  
pp. 343-346
Author(s):  
Fen Xia Zhao
Keyword(s):  
Image Restoration ◽  
Iterative Algorithm ◽  
Moving Objects ◽  
Simulation Experiment ◽  
Motion Blur ◽  
Blurred Images

Motion blur is a phenomenon common to nearly all photographs. Moving objects in the scene or camera movement during the exposure progress lead to blurred images. Lucy Richardson (LR) algorithm has been used to process image restoration of motion blur. In this paper LR algorithm would be discussed. The simulation experiment result shows the PSF plays an important role in the image restoration with LR iterative algorithm.

PSF Estimation and Image Restoration for Motion Blurred Images

2012 ◽  
Vol 562-564 ◽  
pp. 2124-2127 ◽  
Author(s):  
Qi Shen Li ◽  
Jian Gong Chen
Keyword(s):  
Fourier Transform ◽  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Motion Blur ◽  
Blurred Image ◽  
The Fourier Transform ◽  
Original Motion ◽  
Blurred Images ◽  
Blur Direction ◽  
Psf Estimation

Point spread function (PSF) estimation and image restoration algorithm are the hotspots In the research of motion blurred image restoration. In order to improve the efficacy of image restoration, an improved algorithm named quadric transforms (QT) method is proposed in this paper by analyzing the restoration process of motion blurred images. Firstly, Fourier transform and homomorphism transform are applied to the original motion blurred image, and then the Fourier transform and homomorphism transform are used again to the obtained spectrum image. Secondly, the motion blur direction is estimated by Radon transform. Thirdly, the motion blur length is found by differential autocorrelation operations. Finally, utilizing the estimated blur direction and blur length, the motion blurred image is restored by Wiener filtering. Experimental results show that the proposed QT method can get more accurate estimated motion blur angles than the primary transform (PT, that is, Fourier transform and homomorphism transform are used one time) method and can get better restored images under the meaning of peak signal to noise ratio (PSNR).

ARTIFICIAL NEURAL NETWORKS FOR BLUR IDENTIFICATION AND RESTORATION OF NONLINEARLY DEGRADED IMAGES

2001 ◽  
Vol 11 (05) ◽  
pp. 455-461 ◽  
Author(s):  
T. A. CHEEMA ◽  
I. M. QURESHI ◽  
A. JALIL ◽  
A. NAVEED
Keyword(s):  
Image Restoration ◽  
Error Function ◽  
Nonlinear Phenomena ◽  
Model Combining ◽  
Restoration Algorithm ◽  
Blur Identification ◽  
Blurred Image ◽  
Gradient Based ◽  
Blurred Images ◽  
Degraded Images

In this paper, an image restoration algorithm is proposed to identify noncausal blur function. Image degradation processes include both linear and nonlinear phenomena. A neural network model combining an adaptive auto-associative network with a random Gaussian process is proposed to restore the blurred image and blur function simultaneously. The noisy and blurred images are modeled as continuous associative networks, whereas auto-associative part determines the image model coefficients and the hetero-associative part determines the blur function of the system. The self-organization like structure provides the potential solution of the blind image restoration problem. The estimation and restoration are implemented by using an iterative gradient based algorithm to minimize the error function.

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