Image Denoising using K-SVD Algorithm based on Gabor Wavelet Dictionary

Khedr, Wael M.; Ali, R.; Ismail, Fawzan

doi:10.5120/9521-3926

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…To evaluate the performance of our algorithm, it was compared with other algorithms such as: KSVD based on Gabor wavelet [22], DCT [13], log-Gabor [23]. All…”

Section: Numerical Resultsmentioning

confidence: 99%

“…This is done by selecting the best atom from the dictionary that has the largest correlation with residual. The final OMP algorithm is given in algorithm 1 [22].…”

Section: Orthogonal Matching Pursuitmentioning

confidence: 99%

“…It was used to find the best SR [13,21]. There are many algorithms that are developed K-SVD, for example: in [22,23] Gabor wavelet dictionary it was used and it was found that this dictionary is better than a DCT dictionary for image denoising.…”

Section: Introductionmentioning

confidence: 99%

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Medical Image Denoising based on Log-Gabor Wavelet Dictionary and K-SVD Algorithm

Farouk¹,

El-Sayed²,

Aly³

2016

IJCA

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Medical image denoising is the main step in medical diagnosis, which removes the noise without affecting relevant features of the image. There are many algorithms that can be used to reduce the noise such as: threshold and the sparse representation. The K-SVD is one of the most popular sparse representation algorithms, which is depend on Orthogonal Matching Pursuit (OMP) and Discrete Cosine Transform (DCT) dictionary. In this paper, an algorithm for image denoising was designed to develop K-SVD by using Regularized Orthogonal Matching Pursuit (ROMP) over log Gabor wavelet adaptive dictionary. To evaluate the performance of the proposed techniques, the results were compared with the results of DCT and Gabor wavelet dictionary. The numerical results show that the performance of our algorithm is more efficient in medical image denoising. KeywordsSparse representation (SR), K-SVD, log-Gabor wavelet dictionary, regularized orthogonal matching pursuit and orthogonal matching pursuit.

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“…To evaluate the performance of our algorithm, it was compared with other algorithms such as: KSVD based on Gabor wavelet [22], DCT [13], log-Gabor [23]. All…”

Section: Numerical Resultsmentioning

confidence: 99%

“…This is done by selecting the best atom from the dictionary that has the largest correlation with residual. The final OMP algorithm is given in algorithm 1 [22].…”

Section: Orthogonal Matching Pursuitmentioning

confidence: 99%

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Medical Image Denoising based on Log-Gabor Wavelet Dictionary and K-SVD Algorithm

Farouk¹,

El-Sayed²,

Aly³

2016

IJCA

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“…Another important approach that attracted attention in image processing is sparse representation. It has been used for image labeling [34], image segmentation and classification [35], image inpainting [36] and image denoising [37][38][39], among others. This approach was also used for denoising MR images.…”

Section: Introductionmentioning

confidence: 99%

Non-Local SVD Denoising of MRI Based on Sparse Representations

Leal

Zurek

Leal³

2020

Sensors

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Magnetic Resonance (MR) Imaging is a diagnostic technique that produces noisy images, which must be filtered before processing to prevent diagnostic errors. However, filtering the noise while keeping fine details is a difficult task. This paper presents a method, based on sparse representations and singular value decomposition (SVD), for non-locally denoising MR images. The proposed method prevents blurring, artifacts, and residual noise. Our method is composed of three stages. The first stage divides the image into sub-volumes, to obtain its sparse representation, by using the KSVD algorithm. Then, the global influence of the dictionary atoms is computed to upgrade the dictionary and obtain a better reconstruction of the sub-volumes. In the second stage, based on the sparse representation, the noise-free sub-volume is estimated using a non-local approach and SVD. The noise-free voxel is reconstructed by aggregating the overlapped voxels according to the rarity of the sub-volumes it belongs, which is computed from the global influence of the atoms. The third stage repeats the process using a different sub-volume size for producing a new filtered image, which is averaged with the previously filtered images. The results provided show that our method outperforms several state-of-the-art methods in both simulated and real data.

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“…It works by iteratively alternating between sparse coding the input data based on the current dictionary, and updating the atoms in the dictionary to better fit the data. The K-SVD improved with different type of dictionary to form atoms of it such as: DCT (Elad and Aharon, 2006), Gabor Wavelet (Khedr et al, 2012), Log-Gabor and Log Gabor Wavelet (Farouk et al, 2016). In Ruiz-Reyes et al (2005), the space-alternating generalized expectationmaximization (SAGE) algorithm is used to estimate the values of parameter vector and minimum description length.…”

Section: Introductionmentioning

confidence: 99%

Biomedical images enhancement based on swarm optimization and differential evolution technique

Alotaibi¹

2018

Int. j. adv. appl. sci.

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In this paper, we have introduced an effective technique to remove the noise in the MRI and CT medical images during the process of acquisition, transmission, storage or compression. Removing these noise from medical images must be done without affecting relevant features of the image. Many techniques, such as genetic algorithm, Particle Swarm Optimization, Dynamic Multi-Swarm Particle Swarm Optimization and matching pursuit algorithm are used for denoising MRI and CT images. These techniques need more time to remove noise from medical images and find local point optimal. The proposed Differential Evolution based on Matching Pursuit (DE-MP) is used to detect best atom dictionary. The initial dictionary is created from an anisotropic atom. To evaluate the performance of the proposed techniques, the results of the proposed algorithm were compared with the other algorithm. The numerical results show that the performance of the proposed algorithm is more efficient and faster than other algorithms for medical images denoising.

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Image Denoising using K-SVD Algorithm based on Gabor Wavelet Dictionary

Cited by 4 publications

References 9 publications

Medical Image Denoising based on Log-Gabor Wavelet Dictionary and K-SVD Algorithm

Medical Image Denoising based on Log-Gabor Wavelet Dictionary and K-SVD Algorithm

Non-Local SVD Denoising of MRI Based on Sparse Representations

Biomedical images enhancement based on swarm optimization and differential evolution technique

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