Image denoising by supervised adaptive fusion of decomposed images restored using wave atom, curvelet and wavelet transform

Swami, Preety D.; Jain, Alok

doi:10.1007/s11760-012-0343-z

Cited by 31 publications

(8 citation statements)

References 10 publications

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“…Wiener filtering was combined with DT-CWT and continuous wavelet transform (CWT) (to a seismic signal) in [41] and [42], respectively. The area in the image may be defined as a texture, edge or smooth (homogeneous) region [43]. If for each of these areas we chose the filter which gives good result among the existing filters, eventually we get three diverse filters for the image.…”

Section: Hybrid Filteringmentioning

confidence: 99%

An overview of the fundamental approaches that yield several image denoising techniques

et al. 2019

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Digital image is considered as a powerful tool to carry and transmit information between people. Thus, it attracts the attention of large number of researchers, among them those interested in preserving the image features from any factors that may reduce the image quality. One of these factors is the noise which affects the visual aspect of the image and makes others image processing more difficult. Thus far, solving this noise problem remains a challenge for the researchers in this field. A lot of image denoising techniques have been introduced in order to remove the noise by taking care of the image features; in other words, getting the best similarity to the original image from the noisy one. However, the findings are still inconclusive. Beside the enormous amount of researches and studies which adopt several mathematical concepts (statistics, probabilities, modeling, PDEs, wavelet, fuzzy logic, etc.), there is also the scarcity of review papers which carry an important role in the development and progress of research. Thus, this review paper intorduce an overview of the different fundamental approaches that yield the several image-denoising techniques, presented with a new classification. Furthermore, the paper presents the different evaluation tools needed on the comparison between these techniques in order to facilitate the processing of this noise problem, among a great diversity of techniques and concepts.

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Section: Hybrid Filteringmentioning

confidence: 99%

An overview of the fundamental approaches that yield several image denoising techniques

et al. 2019

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“…In addition to improving the process of the OMP algorithm, researchers have been the subjects of intense research for selection of the dictionary. Studies show that the dictionary has a great influence on the performance of the OMP algorithm [34,35]. To date, Gabor dictionary is widely used and has a better performance in signal processing.…”

Section: The Dictionarymentioning

confidence: 99%

Enhanced Orthogonal Matching Pursuit Algorithm and Its Application in Mechanical Equipment Fault Diagnosis

Luo

2017

Shock and Vibration

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The vibration signal measured from the mechanical equipment is associated with the operation of key structure, such as the rolling bearing and gear. The effective signal processing method for early weak fault has attracted much attention and it is of vital importance in mechanical fault monitoring and diagnosis. The recently proposed atomic sparse decomposition algorithm is performed around overcomplete dictionary instead of the traditional signal analysis method using orthogonal basis operator. This algorithm has been proved to be effective in extracting useful components from complex signal by reducing influence of background noises. In this paper, an improved linear frequency-modulated (Ilfm) function as an atom is employed in the proposed enhanced orthogonal matching pursuit (EOMP) algorithm. Then, quantum genetic algorithm (QGA) with the OMP algorithm is integrated since the QGA can quickly obtain the global optimal solution of multiple parameters for rapidly and accurately extracting fault characteristic information from the vibration signal. The proposed method in this paper is superior to the traditional OMP algorithm in terms of accuracy and reducing the computation time through analyzing the simulation data and real world data. The experimental results based on the application of gear and bearing fault diagnosis indicate that it is more effective than traditional method in extracting fault characteristic information.

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“…Generally, the digital images are corrupted by noise artifacts like salt and pepper noise, Gaussian noise, Quantization noise, etc., due to image transmission, compression and acquisition [1]. In image denoising, most of the color and gray scale images are corrupted with Gaussian noise because of higher temperature in electronic circuit and poor illumination.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Based on Statistical Nearest Neighbor and Wave Atom Transform

Suneetha¹,

Reddy²

2021

IJCDS

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In this research, a hybrid multiresolution method is proposed for image denoising, which is the combination of Statistical Nearest Neighbor (SNN) and Wave Atom Transform (WAT) approaches. The proposed model captures both patterns across oscillation and coherence of the pattern along with the oscillations. In this research, KODAK database is utilized for analyzing the proposed model performance, where the acquired color images are contaminated with Gaussian noise of σϵ [5,10,15,20,25,35,40 and 50] and noise range (0, 0.1, 0.35, 0.65, 0.8. 0.9, and 1.0). The denoising performance of the SNN-WAT model is analyzed by means of Gradient Magnitude Similarity Deviation (GMSD), Feature Similarity Index (FSIM), FSIM with chromatic information (FSIMC), Structural Similarity Index (SSIM), Mean SSIM index (MSSIM) and Peak Signal-to-Noise Ratio (PSNR). In the experimental phase, proposed SNN-WAT model averagely enhanced maximum of 4 dB and minimum of 0.1 dB of PSNR compared to the existing models like Non-Local-Means (NLM) with SNN technique.

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Image denoising by supervised adaptive fusion of decomposed images restored using wave atom, curvelet and wavelet transform

Cited by 31 publications

References 10 publications

An overview of the fundamental approaches that yield several image denoising techniques

An overview of the fundamental approaches that yield several image denoising techniques

Enhanced Orthogonal Matching Pursuit Algorithm and Its Application in Mechanical Equipment Fault Diagnosis

Image Denoising Based on Statistical Nearest Neighbor and Wave Atom Transform

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