Image fuzzy enhancement algorithm based on contourlet transform domain

Huang, Yunhu; Chen, De-wang

doi:10.1007/s11042-019-08308-3

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…As a result, the directional information of the image cannot be adequately represented 17 . To eliminate the disadvantage of finite directionality in wavelet transforms, many wavelet‐based multiscale transforms have been presented in the domain of enhancement, such as, Curvelet transformation, 18 Contourlet transformation, 19 nonsubsampled Contourlet transformation (NSCT), 20 Shearlet transformation 21 and nonsubsampled Shearlet transformation (NSST) 22 . Kallel et al 23 presented an approach to enhance dark luminance medical images utilizing singular value decomposition combined with gamma correction and discrete wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

“…This method can effectively remove noise from the original image, but the resulting image is too smooth and unnatural looking. Huang et al 19 presented a contourlet transformation‐based approach for image contrast enhancement. The method is effective in increasing the contrast and brightness of the initial image, but the resulting image is unnatural and visually unsatisfactory.…”

Section: Introductionmentioning

confidence: 99%

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Medical image contrast enhancement based on improved sparrow search algorithm

Fan

Sun

Yin

et al. 2022

Int J Imaging Syst Tech

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The collection or transmission of medical images is often disturbed by various factors, such as insufficient brightness and noise pollution, which will result in the deterioration of image quality and significantly affect the clinical diagnosis.To improve the quality of medical images, a contrast enhancement method based on improved sparrow search algorithm is proposed in this paper. The method is divided into two steps to enhance the medical images. First, a new transform function is introduced to improve the brightness or contrast of medical images, and two parameters in the transform function are optimized by the improved sparrow search algorithm. Second, adaptive histogram equalization method with contrast limited is used to equalize the result image of the previous step to make the pixel distribution of the image more uniform. Finally, a large number of experiments and qualitative and quantitative analyses were conducted on the common data sets. The analysis results demonstrate that the presented approach outperforms some existing medical image processing approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Medical image contrast enhancement based on improved sparrow search algorithm

Fan

Sun

Yin

et al. 2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…In recent years, image fusion has gradually become a hot research topic, and various image fusion algorithms have been proposed. These algorithms can be roughly divided into two categories: spatial and transform domain based algorithms [46].…”

Section: Introductionmentioning

confidence: 99%

A Novel Image Fusion Framework Based on Sparse Representation and Pulse Coupled Neural Network

Zheng

et al. 2019

IEEE Access

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Image fusion techniques are applied to the synthesis of two or more images captured in the same scene to obtain a high-quality image. However, most of the existing fusion algorithms are aimed at single-mode images. To improve the fusion quality of multi-modal images, a novel multi-sensor image fusion framework based on non-subsampled shearlet transform (NSST) is proposed. First, the proposed solution uses NSST to decompose source images into high-and low-frequency components. Then, an improved pulse coupled neural network (PCNN) is proposed to process high-frequency components. Thus, the feature extraction effect of the high-frequency component is meliorated. After that, a sparse representation (SR) based measure, including compact dictionary learning and Max-L1 fusion rule, is designed to enhance the detailed features of the low-frequency component. Finally, the final image is obtained by the reconstruction of high-and low-frequency components via NSST inverse transformation. The proposed method is compared with several existing fusion methods. The experiment results show that the proposed algorithm outperforms other algorithms in both subjective and objective evaluation.

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