Modified Difference Squared Image Based Non Local Means Filter

Mehmood, Rayeesa; Kaur, Amandeep

doi:10.1109/icccnt49239.2020.9225284

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…The neighborhood window centered on

j false(x_{j}, y_{j} false)

slides in the search box, and the similarity between neighborhood windows

N_{i}

and

N_{j}

is calculated as

j

the weighting value. To improve the efficiency of block matching algorithm, the summed square image (SSI) 19 is introduced to reduce the computational complexity of image patch distance while maintaining the matching accuracy.…”

Section: Blind Denoising Using Jplmentioning

confidence: 99%

Dynamic coarse‐to‐fine ISAR image blind denoising using active joint prior learning

Xue

Feng

et al. 2021

Int J Intell Syst

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Most existing nonblind denoising approaches assumed the noise to be homogeneous white Gaussian distribution with known intensity. However, it is difficult to know beforehand or model accurately real‐world noises with complex hybrid distribution and noise intensity. In this paper, active joint prior learning (JPL) is proposed for real‐world ISAR image blind denoising. (1) To explore strong model hierarchy and components relationship automatically, a novel graphical Dirichlet mixture process (GDMP) model is developed, where the latent representations and component hyperparameters are jointly learned from each other. (2) A multiscale joint learning strategy (MJLS) is proposed to take advantage of both the optimization‐ and discriminative learning‐based capabilities. The external noiseless, internal noisy image information and their relationships are jointly explored simultaneously. (3) Low‐rank weighted sparse learning (LWSL) is proposed to learn sparse discriminative correlation components for robust prior learning, and latent low‐rank embedding for GDMP patterns self‐adaptive inference. Extensive experimental results on ISAR image datasets demonstrate the effectiveness of the proposed model for both synthesis and real‐world noisy ISAR images, and the proposed method outperforms the state‐of‐the‐art denoising methods.

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“…The neighborhood window centered on

j false(x_{j}, y_{j} false)

slides in the search box, and the similarity between neighborhood windows

N_{i}

and

N_{j}

is calculated as

j

Section: Blind Denoising Using Jplmentioning

confidence: 99%

Dynamic coarse‐to‐fine ISAR image blind denoising using active joint prior learning

Xue

Feng

et al. 2021

Int J Intell Syst

View full text Add to dashboard Cite

show abstract

“…The first group refers to the methods, utilizing local information in the surrounding of a representative pixel, formed by a local window [22][23][24]. In this category, we can mention bilateral filters, average, median, weighted median filters (WMF), anisotropic diffusion (AD) and edge-avoiding wavelet (EAW) [25,26]. One of the substantial limitations of such local filters is producing artefacts in the form of halos along the image edges [27].…”

Section: Recent Workmentioning

confidence: 99%

Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System

Kubíček

Strycek

Černý

et al. 2021

Sensors

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In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.

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The Cheonji Lake GeoAI Dataset Based in Synthetic Aperture Radar Images: TerraSAR-X, Sentinel-1 and ALOS PALSAR-2

Lee,

Lee

et al. 2023

GEO DATA

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The fluctuations in the area and level of Cheonji in Baekdu Mountain have been employed as significant indicators of volcanic activity. Monitoring these changes directly in the field is challenging because of the geographical and spatial features of Baekdu Mountain. Therefore, remote sensing technology is crucial. Synthetic aperture radar utilizes high-transmittance microwaves to directly emit and detect the backscattering from objects. This weatherproof approach allows monitoring in every climate. Additionally, it can accurately differentiate between water bodies and land based on their distinct roughness and permittivity characteristics. Therefore, satellite radar is highly suitable for monitoring the water area of Cheonji. The existing algorithms for classifying water bodies using satellite radar images are significantly impacted by speckle noise and shadows, resulting in frequent misclassification. Deep learning techniques are being utilized in algorithms to accurately compute the area and boundary of interest in an image, surpassing the capabilities of previous algorithms. This study involved the creation of an AI dataset specifically designed for detecting water bodies in Cheonji. The dataset was constructed using satellite radar images from TerraSAR-X, Sentinel-1, and ALOS-2 PALSAR-2. The primary objective was to accurately detect the area and level of water bodies. Applying the dataset of this study to deep learning techniques for ongoing monitoring of the water bodies and water levels of Cheonji is anticipated to significantly contribute to a systematic method for monitoring and forecasting volcanic activity in Baekdu Mountain.

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Modified Difference Squared Image Based Non Local Means Filter

Cited by 4 publications

References 24 publications

Dynamic coarse‐to‐fine ISAR image blind denoising using active joint prior learning

Dynamic coarse‐to‐fine ISAR image blind denoising using active joint prior learning

Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System

The Cheonji Lake GeoAI Dataset Based in Synthetic Aperture Radar Images: TerraSAR-X, Sentinel-1 and ALOS PALSAR-2

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