Hybrid image denoising method based on non‐subsampled contourlet transform and bandelet transform

Wang, Xiaokai; Chen, Wenchao; Gao, Jinghuai; Wang, Chao

doi:10.1049/iet-ipr.2017.0647

Cited by 20 publications

(7 citation statements)

References 23 publications

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“…In [37] and [38], the nonsubsampled pyramid (NSP) transform and nonsubsampled directional filter bank (NSDFB) were used in the NSCT for multi-scale and multidirectional image decomposition. First, the image was decomposed into low-frequency and high-frequency components by the NSP.…”

Section: A Image Decomposition Using Nsctmentioning

confidence: 99%

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

2021

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In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images.

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Section: A Image Decomposition Using Nsctmentioning

confidence: 99%

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

2021

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“…Experimental results clearly prove that under image processing attacks, technique is robust and have great tradeoff among security, imperceptibility, and robustness. Wang et al introduced a hybrid image denoising technique based on NSCT and bandelet transform. In this method, wavelet transform used in second generation bandelet is replaced with NSCT.…”

Section: Related Workmentioning

confidence: 99%

SPIHT‐based multiple image watermarking in NSCT domain

Kumar

Singh

Kumar

et al. 2018

Concurrency and Computation

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Image watermarking in wavelet domain is a challenging problem as it includes a proper selection of wavelet transforms as well as sub-bands for accurate embedding and recovery of watermarks.However, selection of a suitable wavelet transform for multiple image watermarking is an area of great interest. Therefore, in this work, a multiple watermarking technique using combination of redundant discrete wavelet transforms (RDWT), non-subsampled contourlet transform (NSCT), set partitioning in hierarchical tree (SPIHT), and singular value decomposition (SVD) is proposed. The proposed scheme is aimed to achieve high capacity, robustness, and imperceptibility. The security aspect of the proposed method is enhanced by using Arnold transform. Since RDWT and NSCT are both shift invariant in nature, therefore they are suitable for multiple image watermarking. Furthermore, the proposed method provides flexibility in selection of appropriate sub-bands for watermark embedding and recovery. Experimental results and analysis reveals that the proposed technique gives maximum PSNR, NC, and SSIM value up to 40.97 dB, 1, and 0.9994, respectively. Furthermore, the performance estimation of our technique is found superior to previously reported techniques under consideration. KEYWORDSArnold transform, non-sub sampled contourlet transform, RDWT, SPIHT, SVD INTRODUCTIONRecently, secure transmission of digital data over open/public networks is one of potential challenges. 1 Therefore, there is a great requirement to secure the data transmission over networks. Digital image watermarking is one of promising method for protecting digital data and is widely used for copyright protection and secure communication. 2-4 Image watermarking provides secure transmission of digital data by embedding it into the cover image for content authentications, copyright protection, 5 etc.Discrete wavelet transform (DWT) has been widely used for image watermarking. 6 However, the major drawbacks associated with the DWT are its shift sensitivity and poor directionality that can be avoided by using redundant wavelet transforms. 7 The existing literatures over image watermarking clearly states that the redundant wavelet transforms are suitable for single and multiple images watermarking 7-10 as these transforms do not use down sampling operation in implementation. Thus, no data/information loss occurs during forward and inverse transformation. Furthermore, redundant discrete wavelet transform (RDWT) 11 and non-subsampled contourlet transform (NSCT) 12 are shift invariant. This property enables us to have better reconstruction of images and found useful in many image processing applications. Furthermore, NSCT provides rich directional information, which can be used in embedding single as well as multiple watermarks after forward transformation. 12 It is evident that the performance of combined transform domain techniques (two or more than two) is better than individual one. 7 Thus, being motivated by above facts, an image watermarking technique in NSCT domain using...

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“…In order to fully obtain the direction information, Wang et al proposed contourlet transform for medical image enhancement. However, due to the down-sampling process in the Laplacian pyramid and the directional filter bank, the contourlet transform is limited in translation invariance, and this shortcoming may lead to the pseudo-Gibbs phenomenon around the singularity, which reduces the local information and weakens the characteristics of direction selection [12]. Furthermore, Kollem et al proposed to use nondown-sampled contourlet transform for image enhancement, which has the characteristics of translation invariance so that each pixel of the transformed sub-band corresponds to the pixel of the original image in the same space, while suppressing the interference of new noise [13].…”

Section: Introductionmentioning

confidence: 99%

MIE-NSCT: Adaptive MRI Enhancement Based on Nonsubsampled Contourlet Transform

Liu

Zhao

Dong

et al. 2021

Mathematical Problems in Engineering

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Image enhancement technology is often used to improve the quality of medical images and helps doctors or expert systems identify and diagnose diseases. This paper aimed at the characteristics of magnetic resonance imaging (MRI) with complex and difficult-to-enhance details and to propose a nonsubsampled contourlet transform- (NSCT-) based enhancement algorithm called MIE-NSCT. NSCT was used for MRI sub-band decomposition. For high-pass sub-bands, four fuzzy rules were proposed to enhance multiscale and multidirectional edge contour details from adjacent eight directions, whilst for low-pass sub-bands, a new adaptive histogram enhancement algorithm was proposed. The problem of noise amplification and loss of details during the enhancement process was solved. The algorithm was verified on the public dataset BraTS2017 and compared with other advanced methods. Experimental results showed that MIE-NSCT had obvious advantages in improving the quality of medical images, and high-quality medical images showed enhanced performance in grading tumour. MIE-NSCT is suitable for integration into an interactive expert system to provide support for the visualization of disease diagnosis.

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Hybrid image denoising method based on non‐subsampled contourlet transform and bandelet transform

Cited by 20 publications

References 23 publications

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

SPIHT‐based multiple image watermarking in NSCT domain

MIE-NSCT: Adaptive MRI Enhancement Based on Nonsubsampled Contourlet Transform

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