Effective Five Directional Partial Derivatives-Based Image Smoothing and a Parallel Structure Design

Cho, Choongsang; Lee, Sangkeun

doi:10.1109/tip.2016.2526785

Cited by 20 publications

(19 citation statements)

References 17 publications

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“…One can see that L 0 [47] performs better for recovering flat-regions than TV [37], where TV suffers from the staircase effects. As pointed out in [10], both the results by TV [37] and L 0 [47] may include some noisy pixels near edges; see the zoom-in views. Although NLM-SAP is better than the classical NLM [4], those pixels near edges are not totally in accordance with the clean ones.…”

Section: D Color Imagementioning

confidence: 83%

“…Appendix. The detailed calculation process of (7a), (7b), (8a), (8b), (9a), (9b), (10) and (11) is given in the following.…”

Section: Discussionmentioning

confidence: 99%

“…Note that, we use the same p value for each pixel. The corresponding iterative procedure is summarized in Algorithm 1. from (7a) or (7b) or (8a) or (8b) or (9a) or (9b) or (10) or (11).…”

Section: A Selective Averaging With Multiple Neighbors Methods For 1d mentioning

confidence: 99%

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Piecewise constant signal and image denoising using a selective averaging method with multiple neighbors

Wang¹,

Wu²,

Deng³

2019

Inverse Problems &Amp; Imaging

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Piecewise constant signals and images are an important kind of data. Typical examples include bar code signals, logos, cartoons, QR codes (Quick Response codes), and text images, which are widely used in both general commercial and automotive industry use. One previous work called a general selective averaging method (GSAM) was introduced to remove noise from them. It chooses homogeneous neighbors from the two closest pixels (one pixel at each side) to update the current pixel. One limitation is that it suffered from appearing sparse noisy pixels in the denoised result when the noise level is high. In this paper, we try to solve this problem by proposing a selective averaging method with multiple neighbors. To update the intensity value at each pixel, the proposed algorithm averages more homogeneous neighbors selected from a large domain, which is based on the property of the local geometry of signals and images. This greatly reduces sparse noisy pixels left in the final result by GSAM. Similarly, our method adopts the Neumann boundary condition at edges, and thus preserves edges well. In 1D case, some theoretical results are given to guarantee the convergence of our algorithm. In 2D case, except eliminating additive Gaussian noise, this algorithm can be used for restoring noisy images corrupted by speckle noise. Intensive experiments on both gray and color image denoising demonstrate that the proposed method is quite effective for piecewise constant image denoising and achieves superior performance visually and quantitatively.

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Section: D Color Imagementioning

confidence: 83%

“…Appendix. The detailed calculation process of (7a), (7b), (8a), (8b), (9a), (9b), (10) and (11) is given in the following.…”

Section: Discussionmentioning

confidence: 99%

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Piecewise constant signal and image denoising using a selective averaging method with multiple neighbors

Wang¹,

Wu²,

Deng³

2019

Inverse Problems &Amp; Imaging

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show abstract

“…The SE block can be embedded in any other network model but inevitably increases the number of network parameters and computational cost. Still, many other similar structures have been studied [31][32].…”

Section: B Senet and Sknetmentioning

confidence: 99%

T–S Fuzzy Model Based Multi-Branch Deep Network Architecture

Wang

et al. 2020

IEEE Access

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In the traditional CNN design, the hyperparameters, such as the size of the convolutional kernel and stride, are difficult to determine. In this paper, a new convolutional network architecture, named multi-branch fuzzy architecture network (MBFAN), was proposed for this problem. In MBFAN, some branches with a certain convolutional neural network architecture are connected in parallel. In each branch, a different-sized convolutional kernel is applied. By data training and normalization, a weight is given to each branch. By these weights, the important features in the final output are strengthened. By normalization, the branches were interconnected together, making the training process more efficient. Due to overfitting, with the increase of branches, the MBFAN accuracy increases, and then decreases. The number of branches is optimized when the MBFAN accuracy is highest. On the other hand, the location of the convolutional kernel center in an image has a great influence on the convolutional results. This is also discussed in MBFAN. For the experiments, the proposed MBFAN was adopted and tested in a simple convolutional network and a VGG16 network. INDEX TERMS deep learning, convolutional neural network, multi-branch, fuzzy.

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“…Lamari et al [6] put forward the standard architecture of relational analysis for big data. A study by Cho et al [7] proposed a parallel design scheme. An algorithm using programs to analyze and locate common subexpressions was designed in a study by Kim et al.…”

Section: Introductionmentioning

confidence: 99%

A performance optimization strategy based on degree of parallelism and allocation fitness

Ying

Ban

2018

J Wireless Com Network

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With the emergence of big data era, most of the current performance optimization strategies are mainly used in a distributed computing framework with disks as the underlying storage. They may solve the problems in traditional disk-based distribution, but they are hard to transplant and are not well suitable for performance optimization especially for an in-memory computing framework on account of different underlying storage and computation architecture. In this paper, we first give the definition of the resource allocation model, parallelism degree model, and allocation fitness model on the basis of the theoretical analysis of Spark architecture. Second, based on the model presented, we propose a strategy embedded in the evaluation model which is easy to perform. The optimization strategy selects the worker with a lower load that satisfies requirements to assign the latter tasks, and the worker with a higher load may not be assigned tasks. The experiments consisting of four variance jobs are conducted to verify the effectiveness of the presented strategy.

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Effective Five Directional Partial Derivatives-Based Image Smoothing and a Parallel Structure Design

Cited by 20 publications

References 17 publications

Piecewise constant signal and image denoising using a selective averaging method with multiple neighbors

Piecewise constant signal and image denoising using a selective averaging method with multiple neighbors

T–S Fuzzy Model Based Multi-Branch Deep Network Architecture

A performance optimization strategy based on degree of parallelism and allocation fitness

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