Pixel density based trimmed median filter for removal of noise from surface image

Jana, Bhaskara Rao; Haritha, Thotakura; Baliyan, Anupam; Majji, Sankararao; Deshmukh, Radhika G.; Karanam, Santoshachandra Rao

doi:10.1007/s13204-021-01950-0

Cited by 22 publications

(6 citation statements)

References 15 publications

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“…This section will compare the denoising effects of several classic background noise suppression algorithms in the case of long exposure time of spatial targets, to demonstrate the superiority of our algorithm. These competitive algorithms include a median filtering algorithm (MF), a bilateral filtering algorithm (BF), and non-local mean filtering algorithm (NLmeans), and an improved median filtering algorithm (PDBTMF) [36]. a) Subjective evaluation Figure 8 shows a visual comparison of the processing results between the four competitive algorithms and the algorithm in this article.…”

Section: ) Performance Comparison With Competed Methodsmentioning

confidence: 99%

Non-Uniform Background Noise Suppression Method Based on Improved Total Variation Model for Wide-Field Imaging System

Zhou,

Wu,

Wang

et al. 2024

IEEE Access

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A wide-field surveillance system with a long exposure time has a stronger detectability for dim space targets. However, with the increase in exposure time and working temperature, complex non-uniform background noise containing hot pixels of the detector cannot be ignored, seriously affecting the background and imaging quality. This article studies and proposes a high-performance denoising method, which does not use any prior knowledge of the target and can automatically remove noise from the image. This method is based on an improved total variation model to remove hot pixels and other background mixed noise in widefield system images. Firstly, using the idea of the traditional local contrast method (LCM), we utilize the significant difference in grayscale values between contaminated pixels and neighboring pixels to detect impulse noise, such as the hot pixels in the image. And then, we designed an improved adaptive maximum filtering algorithm to remove unwanted contamination, which protected target information from being lost and optimized pixels that were attacked by impulse noise. Finally, the total variation algorithm is used to eliminate residual background noise, the detector's readout noise, and non-uniform response. The method proposed in this article can effectively filter out hot pixels and non-uniform background noise while preserving the details of target edges. We conducted experiments on a large number of simulated and original images. For star maps captured in long exposure mode, the method proposed in this article has obvious advantages over several competing algorithms. The experimental results show that, compared to competitive algorithms, the algorithm proposed in this article improves PSNR by at least 13.1%, SSIM by at least 0.4%, IEF by at least 5 times, and IQI by at least 9.2%. At the same time, the algorithm in this article achieved a moderate level of computation time.INDEX TERMS wide-field surveillance system; long exposure time; non-uniform correction; local contrast method; maximum filter; total variation

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Section: ) Performance Comparison With Competed Methodsmentioning

confidence: 99%

Non-Uniform Background Noise Suppression Method Based on Improved Total Variation Model for Wide-Field Imaging System

Zhou,

Wu,

Wang

et al. 2024

IEEE Access

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“…Initially, we apply the FT 17 significance detection method to the original image to obtain a saliency map of the target defects. Next, the saliency map is binarized by the Otsu algorithm, 28 but this may induce noise points, so we apply median filtering 29 and morphological operations 30 to remove the noise. Finally, we perform the AND operation between the denoised image and the original image to obtain the defect processing area while retaining the original image features.…”

Section: Proposed Defect Detection Methodsmentioning

confidence: 99%

SR-HorNetYOLO: an efficient algorithm for defect detection of metal gasket end-face

Ding,

Pan,

Zhang

et al. 2023

J. Electron. Imag.

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.Metal gaskets are an essential component of the manufacturing industry, and detecting end-face defects is critical to ensuring their quality. However, current detection methods struggle to detect such defects due to the gaskets’ uneven end-face structure and small defect size. To address such issues, an approach called SR-HorNetYOLO has been proposed. This approach combines traditional machine vision and deep learning techniques. The visual saliency method is used to simplify the image and remove irrelevant features. YOLOv5 is then optimized in three ways: the GhostNet backbone replaces the CSPDarkNet53 backbone to reduce model weight, the self-attention mechanism and bottleneck transformer block improve the precision of detecting small targets, and the feature pyramid is improved through the HorNet network to enhance the predictive performance of the network. Finally, a classification module and position module are employed to classify and locate three-fusion proportion characteristic images. The experimental results show that the SR-HorNetYOLO method achieves a precision of 93.3% and a recall rate of 94.0%.

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“…Filter-based methods use the local information of the center pixel and remove noise according to the numerical relationship between the current pixel and neighboring pixels, such as mean filter [14], median filter [15], and Gaussian filter [16]. Filter-based methods tend to apply to image de-noising with a quick implementation property.…”

Section: Related Work 21 Traditional Methods Of Remote Sensing Image ...mentioning

confidence: 99%

Anisotropic Weighted Total Variation Feature Fusion Network for Remote Sensing Image Denoising

Tan

2022

Remote Sensing

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Remote sensing images are widely applied in instance segmentation and objetive recognition; however, they often suffer from noise, influencing the performance of subsequent applications. Previous image denoising works have only obtained restored images without preserving detailed texture. To address this issue, we proposed a novel model for remote sensing image denoising, called the anisotropic weighted total variation feature fusion network (AWTVF2Net), consisting of four novel modules (WTV-Net, SOSB, AuEncoder, and FB). AWTVF2Net combines traditional total variation with a deep neural network, improving the denoising ability of the proposed approach. Our proposed method is evaluated by PSNR and SSIM metrics on three benchmark datasets (NWPU, PatternNet, UCL), and the experimental results show that AWTVF2Net can obtain 0.12∼19.39 dB/0.0237∼0.5362 higher on PSNR/SSIM values in the Gaussian noise removal and mixed noise removal tasks than State-of-The-Art (SoTA) algorithms. Meanwhile, our model can preserve more detailed texture features. The SSEQ, BLIINDS-II, and BRISQUE values of AWTVF2Net on the three real-world datasets (AVRIS Indian Pines, ROSIS University of Pavia, HYDICE Urban) are 3.94∼12.92 higher, 8.33∼27.5 higher, and 2.2∼5.55 lower than those of the compared methods, respectively. The proposed framework can guide subsequent remote sensing image applications, regarding the pre-processing of input images.

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Pixel density based trimmed median filter for removal of noise from surface image

Cited by 22 publications

References 15 publications

Non-Uniform Background Noise Suppression Method Based on Improved Total Variation Model for Wide-Field Imaging System

Non-Uniform Background Noise Suppression Method Based on Improved Total Variation Model for Wide-Field Imaging System

SR-HorNetYOLO: an efficient algorithm for defect detection of metal gasket end-face

Anisotropic Weighted Total Variation Feature Fusion Network for Remote Sensing Image Denoising

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