Image deblurring by using the estimation of PSF parameters for image devices

Lee, Jehee; Park, Ki Tae; Moon, Young Shik

doi:10.1109/icce.2010.5418797

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Pattern classification using CNN has been reported in. [6] Blurring makes an image indistinct and hazy in outline or appearance. Blurring caused by motion, hazy and out of focus lens of the camera.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we propose an alternate FLANN structure, which has been shown to provide effective identification of nonlinear dynamic systems. For functional expansion of the input pattern, Chebyschev polynomials are used in [5][6] instead of trigonometric functions. Being a single layer neural network, its computational complexity is less intense as compared to (MLP) and can be used for online learning.…”

Section: Introductionmentioning

confidence: 99%

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Image Deblurring With Weiner Hoff Filter With the Estimation of PSF Parameter

Dwibedi¹,

Panda²

2015

jtasr

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ABSTRACT:In image deblurring mathematical models are used to seek or recover the original image. The key issue is that some information assumed to be lost is present in the blurred image. But this information is "hidden" and can only be recovered if the blurring process detail's is known. In this paper the advantage of adaptive filter theory with neural network is combined to create a new deblurring model. The proposed deblurring model deblurr the images with significant improvements. The neural network present in the proposed model is first trained with a set of blurred and original image pair's and once the neural network is trained it is used to deblurr similar kind of blurred images. KEYWORDS: Deblurring, Artificial neural network, Functional link artificial neural network (FLANN), multilayer perceptron (MLP), and Point spread function (PSF). INTRODUCTION:Over the last several years, Image Deblurring has been an active area of research and it has been considered for a variety of applications in signal and Image Processing.Deblurring is an active area research for a long time. This field comes into existence with the advancement of digital photography. When we capture a moving object with a slow capture speed camera or vice versa the image becomes blurred. We need to remove this noise from the image. With the requirements of aerial surveying, digital photography, it has tremendous application.Recently, artificial neural networks (ANN) have emerged as a powerful learning technique to perform complex tasks in highly nonlinear dynamic environments. Some of the prime advantages of using ANN models are: their ability to learn based on the optimization of an appropriate error function and their excellent performance for approximation of nonlinear function. [1] The functional link ANN (FLANN) proposed by Pao [2] has shown that this network can be used for function approximation and pattern classification with a faster convergence rate and lesser computational complexity than an MLP network.The performance of the FLANN for the task of identification of nonlinear systems has been reported. [3] Using trigonometric functions as functional expansion, the superior performance of the FLANN with respect to the MLP network has been obtained. Here, we propose an alternate FLANN structure, which has been shown to provide effective identification of nonlinear dynamic systems. For functional expansion of the input pattern, Chebyschev polynomials are used in [5][6] instead of trigonometric functions. Being a single layer neural network, its computational complexity is less intense as compared to (MLP) and can be used for online learning. Pattern classification using CNN has been reported in. [6] Blurring makes an image indistinct and hazy in outline or appearance. Blurring caused by motion, hazy and out of focus lens of the camera.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Deblurring With Weiner Hoff Filter With the Estimation of PSF Parameter

Dwibedi¹,

Panda²

2015

jtasr

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Image deblurring using saliency detection

Johar

Kalra

2014

2014 Recent Advances in Engineering and Computational Sciences (RAECS)

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Photographic Color Reproduction Based on Color Variation Characteristics of Digital Camera

Kim¹,

Lee²,

Kim³

et al. 2011

KSII TIIS

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In this paper, a new technique for color reproduction based on color variation characteristics of digital camera under a dim light condition is proposed. Generally, photographers should adjust a camera exposure properly for obtaining an image with real color tone of subjects. Thus, in case of taking a picture under a dim light condition, the exposure time of a camera has to be relatively longer than the one under a bright light condition. Although images with real color tone of the subject are obtained, the images may get blurred due to the shaky hands of photographer holding the camera. In order to avoid the blur effect, an input image is captured from a camera set as a short exposure time under a dim light condition. Then we propose a method to reproduce color tone of the dim input image. To this end, color variation characteristics which represent color variations of a digital camera are first extracted by analyzing the Macbeth color checker images taken under various exposure values. Then, a color reproduction is performed by an estimation based on the color variation characteristics. Experimental results have shown that the proposed method has achieved better performance of color reproduction, compared with existing methods.

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Image deblurring by using the estimation of PSF parameters for image devices

Cited by 3 publications

References 2 publications

Image Deblurring With Weiner Hoff Filter With the Estimation of PSF Parameter

Image Deblurring With Weiner Hoff Filter With the Estimation of PSF Parameter

Image deblurring using saliency detection

Photographic Color Reproduction Based on Color Variation Characteristics of Digital Camera

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