An Edge Detection Approach for Images Contaminated with Gaussian and Impulse Noises

Gupta, Ankush; Ganguly, Ayush; Bhateja, Vikrant

doi:10.1007/978-81-322-1000-9_49

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Alternatively, Rician noise is non-additive and tends to produce Rician-distributed image data. As signal-to-noise ratios (SNRs) increase, Rician distributions tend to resemble Gaussian distributions [ 12 , 13 , 14 , 15 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Denoising of Nifti (MRI) Images with a Regularized Neighborhood Pixel Similarity Wavelet Algorithm

Akindele,

Yu,

Kanda

et al. 2023

Sensors

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The recovery of semantics from corrupted images is a significant challenge in image processing. Noise can obscure features, interfere with accurate analysis, and bias results. To address this issue, the Regularized Neighborhood Pixel Similarity Wavelet algorithm (PixSimWave) was developed for denoising Nifti (magnetic resonance imaging (MRI)). The PixSimWave algorithm uses regularized pixel similarity detection to improve the accuracy of noise reduction by creating patches to analyze the intensity of pixels and locate matching pixels, as well as adaptive neighborhood filtering to estimate noisy pixel values by allocating each pixel a weight based on its similarity. The wavelet transform breaks down the image into scales and orientations, allowing a sparse image representation to allocate a soft threshold on its similarity to the original pixels. The proposed method was evaluated on simulated and raw T1w MRIs, outperforming other methods in terms of an SSIM value of 0.9908 for a low Rician noise level of 3% and 0.9881 for a high noise level of 17%. The addition of Gaussian noise improved PSNR and SSIM, with the results indicating that the proposed method outperformed other models while preserving edges and textures. In summary, the PixSimWave algorithm is a viable noise-elimination approach that employs both sparse wavelet coefficients and regularized similarity with decreased computation time, improving the accuracy of noise reduction in images.

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Section: Literature Reviewmentioning

confidence: 99%

Denoising of Nifti (MRI) Images with a Regularized Neighborhood Pixel Similarity Wavelet Algorithm

Akindele,

Yu,

Kanda

et al. 2023

Sensors

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show abstract

“…FPGA based video stabilisation was proposed by Li 30 , where Kalman filter was used effectively. Very good image denoising and quality assessment is provided in [31][32][33][34][35][36][37][38][39][40][41] .…”

Section: Related Workmentioning

confidence: 99%

Efficient Embedded Hardware Architecture for Stabilised Tracking Sighting System of Armoured Fighting Vehicles

Verma¹,

Ghosh²,

Marathe³

et al. 2019

Def. Sc. Jl.

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A line-of-sight stabilised sighting system, capable of target tracking and video stabilisation is a prime requirement of any armoured fighting tank vehicle for military surveillance and weapon firing. Typically, such sighting systems have three prime electro-optical sensors i.e. day camera for viewing in day conditions, thermal camera for night viewing and eye-safe laser range finder for obtaining the target range. For laser guided missile firing, additional laser target designator may be a part of sighting system. This sighting system provides necessary parameters for the fire control computer to compute ballistic offsets to fire conventional ammunition or fire missile. System demands simultaneous interactions with electro-optical sensors, servo sensors, actuators, multi-function display for man-machine interface, fire control computer, logic controller and other sub-systems of tank. Therefore, a complex embedded electronics hardware is needed to respond in real time for such system. An efficient electronics embedded hardware architecture is presented here for the development of this type of sighting system. This hardware has been developed around SHARC 21369 processor and FPGA. A performance evaluation scheme is also presented for this sighting system based on the developed hardware.

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“…N represents total number of pixels present in the input image. This filter has been well applied in recent past to catalyze the performance of edge detector in noisy environments [32]- [33]. Having converted the image into homogeneous and non-homogeneous regions, it is now needed that the processing of these regions be done separately.…”

Section: Repeat and Processmentioning

confidence: 99%

Bilateral despeckling filter in homogeneity domain for breast ultrasound images

Bhateja

Misra

Urooj

et al. 2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Breast sonograms are more effective towards differentiation of cysts from solid tumours; if they could be postprocessed for minimization of speckle content without blurring of edges. The approach presented in this paper consists of a bilateral filtering in homogeneity domain so that the despeckling process do not compromises the texture and features of masses. The proposed despeckled approach decomposes the input image into homogeneous and non-homogeneous regions; which are then selectively processed using the bilateral filter. The domain filtering component is made dominant when applied to homogeneous pixels providing smoothening while the range filter dominates on the non-homogeneous pixels leading to edge preservation. Simulations carried out on breast ultrasound images depict satisfactory speckle filtering supported with improvement in values of performance parameters (PSNR, SSIM & SSI).

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An Edge Detection Approach for Images Contaminated with Gaussian and Impulse Noises

Cited by 9 publications

References 12 publications

Denoising of Nifti (MRI) Images with a Regularized Neighborhood Pixel Similarity Wavelet Algorithm

Denoising of Nifti (MRI) Images with a Regularized Neighborhood Pixel Similarity Wavelet Algorithm

Efficient Embedded Hardware Architecture for Stabilised Tracking Sighting System of Armoured Fighting Vehicles

Bilateral despeckling filter in homogeneity domain for breast ultrasound images

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