• Title/Summary/Keyword: total variation regularization

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Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

  • Zhou, Dabiao;Wang, Dejiang;Huo, Lijun;Jia, Ping
    • Journal of the Optical Society of Korea
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    • v.20 no.6
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    • pp.752-761
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    • 2016
  • Hyperspectral images are often contaminated with stripe noise, which severely degrades the imaging quality and the precision of the subsequent processing. In this paper, a variational model is proposed by employing spectral-spatial adaptive unidirectional variation and a sparse representation. Unlike traditional methods, we exploit the spectral correction and remove stripes in different bands and different regions adaptively, instead of selecting parameters band by band. The regularization strength adapts to the spectrally varying stripe intensities and the spatially varying texture information. Spectral correlation is exploited via dictionary learning in the sparse representation framework to prevent spectral distortion. Moreover, the minimization problem, which contains two unsmooth and inseparable $l_1$-norm terms, is optimized by the split Bregman approach. Experimental results, on datasets from several imaging systems, demonstrate that the proposed method can remove stripe noise effectively and adaptively, as well as preserve original detail information.

ITERATIVE REWEIGHTED ALGORITHM FOR NON-CONVEX POISSONIAN IMAGE RESTORATION MODEL

  • Jeong, Taeuk;Jung, Yoon Mo;Yun, Sangwoon
    • Journal of the Korean Mathematical Society
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    • v.55 no.3
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    • pp.719-734
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    • 2018
  • An image restoration problem with Poisson noise arises in many applications of medical imaging, astronomy, and microscopy. To overcome ill-posedness, Total Variation (TV) model is commonly used owing to edge preserving property. Since staircase artifacts are observed in restored smooth regions, higher-order TV regularization is introduced. However, sharpness of edges in the image is also attenuated. To compromise benefits of TV and higher-order TV, the weighted sum of the non-convex TV and non-convex higher order TV is used as a regularizer in the proposed variational model. The proposed model is non-convex and non-smooth, and so it is very challenging to solve the model. We propose an iterative reweighted algorithm with the proximal linearized alternating direction method of multipliers to solve the proposed model and study convergence properties of the algorithm.

Compressed-sensing (CS)-based Image Deblurring Scheme with a Total Variation Regularization Penalty for Improving Image Characteristics in Digital Tomosynthesis (DTS) (디지털 단층합성 X-선 영상의 화질개선을 위한 TV-압축센싱 기반 영상복원기법 연구)

  • Je, Uikyu;Kim, Kyuseok;Cho, Hyosung;Kim, Guna;Park, Soyoung;Lim, Hyunwoo;Park, Chulkyu;Park, Yeonok
    • Progress in Medical Physics
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    • v.27 no.1
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    • pp.1-7
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    • 2016
  • In this work, we considered a compressed-sensing (CS)-based image deblurring scheme with a total-variation (TV) regularization penalty for improving image characteristics in digital tomosynthesis (DTS). We implemented the proposed image deblurring algorithm and performed a systematic simulation to demonstrate its viability. We also performed an experiment by using a table-top setup which consists of an x-ray tube operated at $90kV_p$, 6 mAs and a CMOS-type flat-panel detector having a $198-{\mu}m$ pixel resolution. In the both simulation and experiment, 51 projection images were taken with a tomographic angle range of ${\theta}=60^{\circ}$ and an angle step of ${\Delta}{\theta}=1.2^{\circ}$ and then deblurred by using the proposed deblurring algorithm before performing the common filtered-backprojection (FBP)-based DTS reconstruction. According to our results, the image sharpness of the recovered x-ray images and the reconstructed DTS images were significantly improved and the cross-plane spatial resolution in DTS was also improved by a factor of about 1.4. Thus the proposed deblurring scheme appears to be effective for the blurring problems in both conventional radiography and DTS and is applicable to improve the present image characteristics.

Depth Upsampling Method Using Total Generalized Variation (일반적 총변이를 이용한 깊이맵 업샘플링 방법)

  • Hong, Su-Min;Ho, Yo-Sung
    • Journal of Broadcast Engineering
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    • v.21 no.6
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    • pp.957-964
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    • 2016
  • Acquisition of reliable depth maps is a critical requirement in many applications such as 3D videos and free-viewpoint TV. Depth information can be obtained from the object directly using physical sensors, such as infrared ray (IR) sensors. Recently, Time-of-Flight (ToF) range camera including KINECT depth camera became popular alternatives for dense depth sensing. Although ToF cameras can capture depth information for object in real time, but are noisy and subject to low resolutions. Recently, filter-based depth up-sampling algorithms such as joint bilateral upsampling (JBU) and noise-aware filter for depth up-sampling (NAFDU) have been proposed to get high quality depth information. However, these methods often lead to texture copying in the upsampled depth map. To overcome this limitation, we formulate a convex optimization problem using higher order regularization for depth map upsampling. We decrease the texture copying problem of the upsampled depth map by using edge weighting term that chosen by the edge information. Experimental results have shown that our scheme produced more reliable depth maps compared with previous methods.

Mathematical Model for Acousto-Optical Tomography and Its Numerical Simulation (음향광학 단층촬영(Acousto-Optical Tomography)의 수학적 모델과 수치해석적 시뮬레이션)

  • Nam, Hae-Won;Hur, Jang-Yong;Kim, So-Young;Lee, Re-Na
    • Progress in Medical Physics
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    • v.23 no.1
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    • pp.42-47
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    • 2012
  • In this paper, Acousto-Optical tomography is modeled by a linear integral equation and an inverse problem involving a diffusion equation in n-spatial dimensions. We make two-step mathematical model. First, we solve a linear integral equation. Assuming the optical energy fluence rate has been recovered from the previous equation, the absorption coefficient ${\mu}$ is then reconstructed by solving an inverse problem. Numerical experiments are presented for the case n=2. The traditional gradient descent method is used for the numerical simulations. The result of the gradient descent method produces the blurring effect. To get rid of the blurring effect, we suggest the total variation regularization for the minimization problem.