• Title/Summary/Keyword: Deblurring

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IMAGE RESTORATION BY THE GLOBAL CONJUGATE GRADIENT LEAST SQUARES METHOD

  • Oh, Seyoung;Kwon, Sunjoo;Yun, Jae Heon
    • Journal of applied mathematics & informatics
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    • v.31 no.3_4
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    • pp.353-363
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    • 2013
  • A variant of the global conjugate gradient method for solving general linear systems with multiple right-hand sides is proposed. This method is called as the global conjugate gradient linear least squares (Gl-CGLS) method since it is based on the conjugate gradient least squares method(CGLS). We present how this method can be implemented for the image deblurring problems with Neumann boundary conditions. Numerical experiments are tested on some blurred images for the purpose of comparing the computational efficiencies of Gl-CGLS with CGLS and Gl-LSQR. The results show that Gl-CGLS method is numerically more efficient than others for the ill-posed problems.

COMPARISON OF INTERPOLATION METHODS for MEDICAL IMAGING (Medical imaging을 위한 영상 보간 방법의 비교)

  • Lee, Byeong-Kil;Ha, Yeong-Ho
    • Proceedings of the KOSOMBE Conference
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    • v.1990 no.11
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    • pp.38-41
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    • 1990
  • A new spline function for resampling discrete signal adaptively is proposed. In general, B-spline function is used for an image interpolation because of its smoothness and continuity, but accompanies a large amount of blurring effect. Hence, we developed a new spline function to remedy this effect, with two procedures ; deblurring of Gaussian blurring and diminishing of aliasing effect caused by deblurring procedure. The proposed function has a parametric expression with $\alpha$ which is related to the variance of Gaussian blurring model. Locally adaptive resampling scheme is obtained by changing a according to statistical characteristics of an image. The proposed, interpolation function shows edge-sharpening effect as well as noise smoothing, with comparison to the conventional schemes.

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A WEIGHTED GLOBAL GENERALIZED CROSS VALIDATION FOR GL-CGLS REGULARIZATION

  • Chung, Seiyoung;Kwon, SunJoo;Oh, SeYoung
    • Journal of the Chungcheong Mathematical Society
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    • v.29 no.1
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    • pp.59-71
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    • 2016
  • To obtain more accurate approximation of the true images in the deblurring problems, the weighted global generalized cross validation(GCV) function to the inverse problem with multiple right-hand sides is suggested as an efficient way to determine the regularization parameter. We analyze the experimental results for many test problems and was able to obtain the globally useful range of the weight when the preconditioned global conjugate gradient linear least squares(Gl-CGLS) method with the weighted global GCV function is applied.

PRECONDITIONED GL-CGLS METHOD USING REGULARIZATION PARAMETERS CHOSEN FROM THE GLOBAL GENERALIZED CROSS VALIDATION

  • Oh, SeYoung;Kwon, SunJoo
    • Journal of the Chungcheong Mathematical Society
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    • v.27 no.4
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    • pp.675-688
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    • 2014
  • In this paper, we present an efficient way to determine a suitable value of the regularization parameter using the global generalized cross validation and analyze the experimental results from preconditioned global conjugate gradient linear least squares(Gl-CGLS) method in solving image deblurring problems. Preconditioned Gl-CGLS solves general linear systems with multiple right-hand sides. It has been shown in [10] that this method can be effectively applied to image deblurring problems. The regularization parameter, chosen from the global generalized cross validation, with preconditioned Gl-CGLS method can give better reconstructions of the true image than other parameters considered in this study.

The Vaguelette-Curvelet Decomposition for Image Deblurring

  • Cho, Changhun;Katsaggelos, Aggelos K.;Paik, Joonki
    • IEIE Transactions on Smart Processing and Computing
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    • v.2 no.3
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    • pp.140-147
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    • 2013
  • We present a vaguelette-curvelet decomposition based image deblurring algorithm. We first perform denoising based on the hard-thresholding rule by estimating unknown curvelet coefficients. The proposed algorithm then calculates vaguelette functions by deconvolving the curvelet bases by the point spread function. Vaguelette transform is finally performed to make a clearly restored image. Since the proposed algorithm uses the curvelet transform to sensitively express the edges in all directions, it is possible to restore images with more naturally preserved edges in all directions.

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Robust Decoding of Barcode Signals Acquired under Nonuniform Illumination (불균일 조명 하에서 획득된 바코드 신호의 강인 복원)

  • Lee, Han-A;Kim, Jeong-Tae
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.4
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    • pp.718-724
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    • 2008
  • We present a novel joint barcode deblurring and nonuniform illumination compensation algorithm for barcode signals whose number of modules is known. The proposed algorithm is based on a penalized least squares method using a roughness penalty function for an illumination model and a double well penalty function for a barcode signal model. In simulations, the proposed method shows an improved performance compared with a conventional method without compensating nonuniform illumination effects. In addition, the proposed method converges quickly during optimization(within 15 iterations), thereby showing strong possibility for real time decoding of barcode signals.

A Study on the Optical Image Deblurring by Spectrum Analysis (스펙트럼 분석에 의한 광영상 복원에 관한 연구)

  • Lee, Seung Hyun;Kim, Eun Soo;Young, In Eung
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.23 no.4
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    • pp.529-534
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    • 1986
  • A holographic iverse filter has been used for optical image deblurring by modeling the PSF(point spread function)of blurred system. This paper presents the optical image restoration of an accidentally blurred photograph by using a simgle inverse filter which is obtained from a blurrec PSF by analyzing the Fourier spectrum of the supposely defocused photograph itself. The filter, a positive transparency having gamma of 2, has amplitude and phase informations simulatneously.

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Adversarial Framework for Joint Light Field Super-resolution and Deblurring (라이트필드 초해상도와 블러 제거의 동시 수행을 위한 적대적 신경망 모델)

  • Lumentut, Jonathan Samuel;Baek, Hyungsun;Park, In Kyu
    • Journal of Broadcast Engineering
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    • v.25 no.5
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    • pp.672-684
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    • 2020
  • Restoring a low resolution and motion blurred light field has become essential due to the growing works on parallax-based image processing. These tasks are known as light-field enhancement process. Unfortunately, only a few state-of-the-art methods are introduced to solve the multiple problems jointly. In this work, we design a framework that jointly solves light field spatial super-resolution and motion deblurring tasks. Particularly, we generate a straight-forward neural network that is trained under low-resolution and 6-degree-of-freedom (6-DOF) motion-blurred light field dataset. Furthermore, we propose the strategy of local region optimization on the adversarial network to boost the performance. We evaluate our method through both quantitative and qualitative measurements and exhibit superior performance compared to the state-of-the-art methods.