RETRACTED ARTICLE: Image denoising and despeckling methods for SAR images to improve image enhancement performance: a survey

Ponmani, E.; Saravanan, P.

doi:10.1007/s11042-021-10871-7

Cited by 19 publications

(5 citation statements)

References 86 publications

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“…Denoising is an important step in image enhancement. Currently, there are various denoising methods, including bilateral filtering-based denoising, Gaussian filtering-based denoising, and linear guided filtering-based denoising [18]. The denoising method based on bilateral filtering is computationally complex and slow.…”

Section: B Image Brightness Equalization Enhancement Methods Based On...mentioning

confidence: 99%

Brightness Equalization Algorithm for Chinese Painting Pigments in Low-Light Environment Based on Region Division

Cheng

2024

IJACSA

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With the promotion and development of Chinese painting and the advancement of photography technology, people can appreciate various types of Chinese paintings through image and other methods. However, Chinese painting images in low-light environments face the problem of extreme uneven brightness distribution. The currently proposed solutions for this problem are not sufficient. Therefore, this research proposes a brightness equalization algorithm for Chinese painting pigments in low-light environments based on region division. This algorithm also utilizes guided filtering for image denoising. In performance testing, the proposed method has a runtime of 16.63 seconds under a scaling factor of 1 and a runtime of 8.37 seconds under a scaling factor of 0.1, which are the fastest among the compared algorithms. In simulation experiments, the brightness equalization value of the proposed method is 198.93, which is listed at the best among all the compared algorithms. This research provides a valuable research direction for the brightness equalization of Chinese painting pigments.

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Section: B Image Brightness Equalization Enhancement Methods Based On...mentioning

confidence: 99%

Brightness Equalization Algorithm for Chinese Painting Pigments in Low-Light Environment Based on Region Division

Cheng

2024

IJACSA

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“…Figure 5(f) shows that our proposed algorithm efficiently suppress speckle noise while maintaining all the imaged targets. The obtained results are evaluated using diverse quantitative metrics: Equivalent Number of Looks (ENL), Structural Similarity Index (SSIM), PSNR, and Root Mean Square Error (RMSE) [25]. According to Table 1, the best ENL value is achieved by our algorithm showing both high speckle reduction capability and radiation characteristics preservation.…”

Section: Comparative Studymentioning

confidence: 99%

A New Compressive Sensing Method for Speckle Reducing in Complex-valued SAR Data

Gherbi,

Bouaraba,

Benssalah

et al. 2023

PIER M

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High resolution Synthetic Aperture Radar (SAR) images are affected by speckle noise, which considerably reduces their visibility and complicates the target identification. In this paper, a new Compressive Sensing (CS) method is proposed to reduce the speckle noise effects of complex valued SAR images. The sparsity of the SAR images allows solving the CS problem using Multiple Measurements Vector (MMV) configuration. Therefore, a special weighted norm is constructed to solve the optimization problem, so that the Variance-Based Joint Sparsity (VBJS) model is used to calculate the weights. An efficient Alternating Direction Method of Multipliers (ADMM) is developed to solve the optimization problem. The obtained results using raw complex-valued measurements with ground truth demonstrate the effectiveness of the proposed despeckling method in terms of both image quality and computational cost.

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“…To provide a solution, speckle filtering was applied, with non-Gaussian multiplicative noise, which indicated that the pixel values did not follow a normal distribution. Consequently, the 7 × 7 Lee [87] filter was used to standardize the image and reduce this problem (see Figure 5c). • Geometric calibration.…”

Section: Image Acquisitionmentioning

confidence: 99%

Sentinel-1 SAR Images and Deep Learning for Water Body Mapping

Pech-May

Aquino-Santos²,

Delgadillo-Partida³

2023

Remote Sensing

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Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing losses and negative consequences for the rural, urban, livestock, agricultural, and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth’s surface and geospatial information processing tools useful for environmental and forest monitoring, climate change impacts, risk analysis, and natural disasters. This paper presents a strategy for the classification of flooded areas using satellite images obtained from synthetic aperture radar, as well as the U-Net neural network and ArcGIS platform. The study area is located in Los Rios, a region of Tabasco, Mexico. The results show that U-Net performs well despite the limited number of training samples. As the training data and epochs increase, its precision increases.

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RETRACTED ARTICLE: Image denoising and despeckling methods for SAR images to improve image enhancement performance: a survey

Cited by 19 publications

References 86 publications

Brightness Equalization Algorithm for Chinese Painting Pigments in Low-Light Environment Based on Region Division

Brightness Equalization Algorithm for Chinese Painting Pigments in Low-Light Environment Based on Region Division

A New Compressive Sensing Method for Speckle Reducing in Complex-valued SAR Data

Sentinel-1 SAR Images and Deep Learning for Water Body Mapping

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