Projection-Based Regularized Dual Averaging for Stochastic Optimization

Ushio, Asahi; Yukawa, Masahiro

doi:10.1109/tsp.2019.2908901

Cited by 9 publications

(2 citation statements)

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“…At the reconstruction end, a RL algorithm based on nonlinear projection for the total difference regularization term is proposed. The nonlinear projection on a closed convex set [14,15] can be used to replace the derivation of the regular term, Thus, more texture details can be retained. The experimental results also show that the proposed algorithm is better than RL-TV algorithm in reconstruction effect.…”

Section: Introductionmentioning

confidence: 99%

An Image Reconstruction Model under Poisson Noise Using multiscale Compressed Sensing

2020

International Journal of Circuits, Systems and Signal Processing

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For the influence of poisson noise images, in order to get rid of poisson noise, this paper put forward image reconstruction method by using multiscale compressed sensing. the algorithm can approximate the optimal sparse representation of the image edge details such as the characteristics of theShearlet domain based multi-scale compressed sensing method. The image is decomposed into the high-frequency subbands byShearlet, and the compressed sensing is applied into each subband to reconstruct the image. In this paper, A total variation of RL iterative algorithm constructed by nonlinear projection algorithm based on closed convex set is explored as the reconstruction method, which use derivation of the nonlinear projection instead of total variation. In mathematics, Shearlet has been proved to be a better tool for edge characterization than traditional wavelet. By using the nonlinear projection scheme to constrain the residual coefficients in the Shearlet domain, a better estimation can be obtained from the Shearlet representation. Numerical examples show that the denoising effect of these methods is very good, which is better than the correlation method based on Curvelet transform. In addition, the number of iterations required by our scheme is far less than that of our competitors.

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Section: Introductionmentioning

confidence: 99%

An Image Reconstruction Model under Poisson Noise Using multiscale Compressed Sensing

2020

International Journal of Circuits, Systems and Signal Processing

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“…GCV method is based on the philosophy that if an arbitrary element of the observed data is omitted, then the corresponding regularized solution should predict this observation well. The choice of regularization parameter should be independent of an orthogonal transformation of the observed data [11], [12]. The advantage of this algorithm is that the data-driven approach is completely used; optimization can be implemented without any prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Multiple Parameters Determination for Image and Video Reconstruction Using Genetic Algorithm and Generalized Stein’s Unbiased Risk Estimation

Dou¹,

Sun²,

Ding³

2019

IEEE Access

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This paper proposes a heuristic multi-parameter optimization method for image and video reconstruction. The method is embedded in an evolutionary computation framework in which a set of parameters are encoded into chromosomes of an individual, and a generalized Stein's unbiased risk estimation (GSURE) is employed as a fitness function. By using the proposed method, the near-optimal parameters can be determined after several iterations, and with the use of these parameters, the ideal image can then be reconstructed. Furthermore, this method can be extended to video reconstruction. According to the similarity between adjacent frames of a video, the generation of population is vividly replaced by the frame index. We calculate the fitness based on the current frame, breed a new generation selectively, and use the new population to optimize continually based on the next frame. After several frames, the parameters can be optimized, and an ideal reconstructed frame can then be steadily output. Numerous experiments demonstrate the effectiveness and advantages of the proposed method.

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An effective cost-sensitive sparse online learning framework for imbalanced streaming data classification and its application to online anomaly detection

et al. 2022

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Projection-Based Regularized Dual Averaging for Stochastic Optimization

Cited by 9 publications

References 50 publications

An Image Reconstruction Model under Poisson Noise Using multiscale Compressed Sensing

An Image Reconstruction Model under Poisson Noise Using multiscale Compressed Sensing

Multiple Parameters Determination for Image and Video Reconstruction Using Genetic Algorithm and Generalized Stein’s Unbiased Risk Estimation

An effective cost-sensitive sparse online learning framework for imbalanced streaming data classification and its application to online anomaly detection

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