Microarray Image Denoising Using Complex Gaussian Scale Mixtures of Complex Wavelets

Srinivasan, Lakshmi; Rakvongthai, Yothin; Oraintara, S.

doi:10.1109/jbhi.2014.2318279

Cited by 17 publications

(10 citation statements)

References 24 publications

(30 reference statements)

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“…The experimental results show that this method can not only remove the noise without blurring of edges and important characteristics of images but also highlight the characteristics of image edge compared with the existing methods. The denoised images obtain higher PSNR and higher SSIM; what is more, the denoising effect is better than the previous article [23][24][25][26][27][28]; hence, the method is of great application value.…”

Section: Introductionmentioning

confidence: 80%

“…In Figure 1, the image processed by [23,24,27] or [28] is more blurred because it loses much edge information. The image processed by [25] or [26] includes much noise which destroys the clarity of the image.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…Figure 2 is an example of Lena, Barbara, and Boat image with the size of 512 × 512 pixels. We compare denoising results with articles [23][24][25][26][27][28] from qualitative and quantitative aspects. The denoised images are shown in Figure 2 (the noise standard deviation = 30), and the comparison results of denoised index PSNR and SSIM are shown in Table 2.…”

Section: Simulation Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

Wang

Liu

2017

Mathematical Problems in Engineering

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Wavelet transform is an effective method for removal of noise from image. But traditional wavelet transform cannot improve the smooth effect and reserve image's precise details simultaneously; even false Gibbs phenomenon can be produced. This paper proposes a new image denoising method based on adaptive multiscale morphological edge detection beyond the above limitation. Firstly, the noisy image is decomposed by using one wavelet base. Then, the image edge is detected by using the adaptive multiscale morphological edge detection based on the wavelet decomposition. On this basis, wavelet coefficients belonging to the edge position are dealt with with the improved wavelet domain wiener filtering, and the others are dealt with with the improved Bayesian threshold and the improved threshold function. Finally, wavelet coefficients are inversely processed to obtain the denoised image. Experimental results show that this method can effectively remove the image noise without blurring edges and highlight the characteristics of image edge at the same time. The validation results of the denoised images with higher peak signal to noise ratio (PSNR) and structural similarity (SSIM) demonstrate their robust capability for real applications in the future.

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Section: Introductionmentioning

confidence: 80%

Section: Simulation Experimentsmentioning

confidence: 99%

Section: Simulation Experimentsmentioning

confidence: 99%

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A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

Wang

Liu

2017

Mathematical Problems in Engineering

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“…DNA microarray image analysis is an active research field [7]- [13]. As new analysis techniques are developed, it will be possible to re-analyze existing images to obtain more accurate genetic data.…”

Section: (A)mentioning

confidence: 99%

“…Therefore, lossy compression methods applied to DNA microarray images should aim to minimize the impact on this type of analysis. As mentioned previously, certain aspects of the analysis have been the subject of intensive recent research [7]- [13]. The proposed Relative Quantizer is designed specifically to minimize the impact to segmentation and to the ratios of spot intensities (CRM), regardless of the specific algorithmic details used within the analysis process.…”

Section: The Relative Quantizer a Motivationmentioning

confidence: 99%

Analysis-Driven Lossy Compression of DNA Microarray Images

Hernández-Cabronero

Blanes

Pinho

et al. 2016

IEEE Trans. Med. Imaging

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Abstract-DNA microarrays are one of the fastest-growing new technologies in the field of genetic research, and DNA microarray images continue to grow in number and size. Since analysis techniques are under active and ongoing development, storage, transmission and sharing of DNA microarray images need be addressed, with compression playing a significant role. However, existing lossless coding algorithms yield only limited compression performance (compression ratios below 2:1), whereas lossy coding methods may introduce unacceptable distortions in the analysis process. This work introduces a novel Relative Quantizer (RQ), which employs non-uniform quantization intervals designed for improved compression while bounding the impact on the DNA microarray analysis. This quantizer constrains the maximum relative error introduced into quantized imagery, devoting higher precision to pixels critical to the analysis process. For suitable parameter choices, the resulting variations in the DNA microarray analysis are less than half of those inherent to the experimental variability. Experimental results reveal that appropriate analysis can still be performed for average compression ratios exceeding 4.5:1.

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Patch-Based Denoising with K-Nearest Neighbor and SVD for Microarray Images

Kuzhali

Suresh²

2018

Advances in Intelligent Systems and Computing

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Microarray Image Denoising Using Complex Gaussian Scale Mixtures of Complex Wavelets

Cited by 17 publications

References 24 publications

A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

Analysis-Driven Lossy Compression of DNA Microarray Images

Patch-Based Denoising with K-Nearest Neighbor and SVD for Microarray Images

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