New image-restoration method using a simultaneous algebraic reconstruction technique: comparison with the Richardson–Lucy algorithm

Murase, Kenya

doi:10.1007/s12194-020-00595-y

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…Image enhancement algorithm only takes into account the numerical level of operation [19][20][21][22], although it improves the image tone and contrast to a certain extent, but it does not go deep into the essence of defogging, so it is not enough to only use image enhancement algorithm, image enhancement based on image restoration can achieve good results [23][24][25]. Based on the physical model, the dark channel prior theory deduces the unknown parameters of the model with reasonable assumptions to re-store the fog free image.…”

Section: Dark Channel Prior Eorymentioning

confidence: 99%

“…M V represents the average image saturation, M represents the maximum image saturation, and m represents the minimum image saturation. e drawing of formula (23) makes the images of different brightness enhanced. After the brightness of the image is enhanced, and the saturation is corrected in HSV space, the image can be enhanced by converting the image from HSV space to RGB space.…”

Section: Combining Retinex Algorithmmentioning

confidence: 99%

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[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Sun

Wang

et al. 2022

Journal of Environmental and Public Health

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According to the characteristics of foggy images, such as high noise, low resolution, and uneven illumination, an improved foggy image enhancement method based on dark channel priority is proposed. First, the new algorithm refines the transmittance and optimizes the atmospheric light value and converts the restored image to HSV space. Second, the brightness V component is enhanced by MSRCR algorithm improved by bilateral filtering, and the saturation S is improved by adaptive stretching algorithm. Finally, the image is converted from HSV space to RGB space to complete image enhancement. The new method solves the problems of that the color of large area is uneven and the overall color of the image is dark when the traditional dark channel prior method is used to remove fog. The experimental results show that from subjective evaluation and quantitative analysis the new algorithm overcomes the shortcomings of noise amplification and edge blur when the conventional enhancement algorithm enhances the image. It can improve image darkening and avoid image distortion in JPEG, BMP, GIF, PNG, PSD, and TIFF formats. By comparing with other image enhancement algorithms, the improved algorithm performs better than DCP, SSR, MSR, MSRCR, and CLAHE algorithm in PSNR, SSIM, and IE evaluation indexes. It has a good effect on preserving the edge information and has good adaptability and stability for heavily polluted haze image enhancement.

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Section: Dark Channel Prior Eorymentioning

confidence: 99%

Section: Combining Retinex Algorithmmentioning

confidence: 99%

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

Sun

Wang

et al. 2022

Journal of Environmental and Public Health

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“…We execute 80 computations, and the means of the image errors are shown in figure 6. The variances of the image error of the TSBI algorithm are shown in table 11.…”

Section: Casementioning

confidence: 99%

“…In comparison to the non-iterative algorithms, the iterative algorithm often has higher imaging accuracy. Common iterative algorithms include the algebraic reconstruction technique (ART) [11], the simultaneous iterative reconstruction technique (SIRT) [12], the Landweber algorithm [13], the conjugate gradient (CG) method [14], the gene algorithm (GA) [15], the L 1 regularization technique (LRT) [16], the nuclear norm regularization technique (NNRT) [17], etc. The ART only uses a set of projection data for each iteration, which has a lower computational cost [18].…”

Section: Introductionmentioning

confidence: 99%

A novel computational imaging algorithm based on split Bregman iterative for electrical capacitance tomography

Qing

Shi

Chai

et al. 2021

Meas. Sci. Technol.

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As an advanced detection technology in the industrial field, electrical capacitance tomography (ECT) can better reconstruct the material distribution state in the measured area by selecting the appropriate algorithm. In order to improve the reconstruction quality, this paper devises a novel objective function to model the ECT image reconstruction problem, in which L 1 -norm is deployed as data fidelity with the focus on weakening the influence of capacitance outliers on the reconstruction quality, L P regularization reinforces the sparseness of the phantom objects, and L 1 regularization applies to model deviation variables to increase the robustness of the system. Based on the fast-iterative shrinkage thresholding technique and the soft thresholding method as sub-solvers, the split Bregman iterative method is designed as an effective solver for the proposed objective function. Numerical simulation and experimental validate that the proposed algorithm has excellent imaging ability compared with other imaging algorithms, which provides a good choice for the practical application of ECT in the future.

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Image Enhancement Method Based on Dark Channel Prior

Shi

Wang

et al. 2022

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)

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New image-restoration method using a simultaneous algebraic reconstruction technique: comparison with the Richardson–Lucy algorithm

Cited by 4 publications

References 26 publications

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision

A novel computational imaging algorithm based on split Bregman iterative for electrical capacitance tomography

Image Enhancement Method Based on Dark Channel Prior

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