Total variation denoising method to improve the detection process in IR images

Chato, Lina; Latifi, Shahram; Kachroo, Pushkin

doi:10.1109/uemcon.2017.8249109

Cited by 6 publications

(8 citation statements)

References 22 publications

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“…To verify that infrared images denoising model in this paper retains more image details, the denoising model proposed in this paper is analyzed in a series of comparisons with the improved total variational infrared image denoising model proposed in Ref. 16, infrared images denoising model based on improved nonlocal mean filtering algorithm proposed in Ref. 41, the infrared image denoising model based on dual-tree complex wavelet and morphology proposed in Ref.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

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Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

2023

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The traditional fractional order total variational model has better results in denoising and maintaining texture details in infrared images. However, it is difficult to determine the order of fractional order differentiation in image processing so that the model has the best denoising effect. To solve this problem, a fractional order total variational infrared image denoising model incorporating a flower pollination particle swarm optimization (PSO) algorithm is proposed in this paper. The model combines the search advantages of the flower pollination optimization algorithm and the PSO algorithm. The maximization multiobjective equation is designed as the fitness function of the optimization algorithm. The optimal order of the fractional order total variational model is found adaptively according to different features in different regions of the infrared image. The experimental results show that the improved model not only achieves the adaptivity of the adaption of the fractional order of total variational model order but also effectively removes the noise and retains the texture structure of infrared images to the maximum extent.

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Section: Experiments and Results Analysismentioning

confidence: 99%

“…proposed a nonuniform infrared image correction algorithm based on curvature filtering and partial differential equations. Chato et al 16 . proposed a method to remove noise from infrared images by minimizing the total variance of the image and the best numerical method.…”

Section: Introductionmentioning

confidence: 99%

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

2023

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“…Research findings, on the other hand, suggest that an OCT image could be well assessed by the retinal tissue area than central retinal thickness with CME. 5 As OCT images contain speckle noise, several techniques of denoising an image have been developed in the last decade such as Gaussian filtering, bilateral filtering, 6 median filtering, 7 anisotropic filtering, 8 total variation denoising, 9 and nonlocal means (NLM) denoising 10 to name a few. These techniques have some limitations like blurred edges and texture of the image, degrade image details, computationally expensive, and corrupted by artifacts due to high impulse noise in an image.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning based automated detection of intraretinal cystoid fluid

Ahmed

Panhwar

Baqai

et al. 2021

Int J Imaging Syst Tech

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The diversified ocular disorders in which cystoid macular edema (CME) occurs, are strongly associated with the vision loss. Optical coherence tomography (OCT) scans that allow screening of the retina, contain artifacts including blur‐edges, speckle noise, and so forth, which create difficulty in identifying retinal fluid. In this work, major image preprocessing techniques such as minimum filtering, block‐matching and 3D filtering, and Richardson–Lucy deconvolution method are applied to minimize noise and other degradation effects from OCT scans, ensured by maintaining image quality assessment scores, that is, blind‐less image spatial quality evaluator and sharpness estimation score. Furthermore, this work develops an automated method based on deep learning (DL) to detect the presence and progression of retinal fluid, that is, CME and its quantification. Specifically, U‐net model with efficient tuning of hyperparameters is proposed to obtain optimal results. The algorithm's performance is evaluated for accurate identification of fluid localization in the case of diabetic macular edema (DME), having CME regions, and compared against the manual segmentation of fluid by experts, which are highly correlated. This enhanced DL method achieves better performance than the algorithms previously reported. The proposed algorithm is evaluated on several performance metrics during training and reports the best score of 99.81, 0.50, 83.34, 86.56, 80.89, and 99.48% for model accuracy, binary cross‐entropy loss, Dice coefficient (DC), precision, recall, and area under the ROC‐curve, respectively. Additionally, fourfold cross‐validation technique is also applied and an average DC score of 84.00% is obtained. This approach can be helpful to prevent vision loss.

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“…a) Fluctuations Smoothing by The TVD-MM Algorithm: In general, the variational calculus is employed to find local extrema in a functional by solving differential equations. Signal denoising is one of its essential applications [38]. An important aspect of signal denoising is to preserve signal features, and also identify signal trends.…”

Section: ) Improved Pb-acd Technique By Signal Smoothingmentioning

confidence: 99%

A Wideband Spectrum Sensing Approach for Cognitive Radios Based on Cepstral Analysis

Moawad

Yao²,

Mansour

et al. 2020

IEEE Open J. Commun. Soc.

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Multiband spectrum access plays an essential role in cognitive radio systems so as to increase the network's throughput through wideband spectrum sensing. It includes identifying the number of subbands comprising a wide spectrum by edge detection, and also examining their occupancy through primary user detection techniques. Despite the offered accuracy of the wavelet-based approaches, their complexity becomes a drawback. Remarkably, the features revealing property of cepstral analysis and its implementation simplicity make it a suitable candidate for signal detection. Motivated by these reasons, this paper presents a wideband spectrum sensing approach based on cepstral analysis. First, we propose the differential log spectral density algorithm for the edge detection phase in order to detect the spectral boundaries within the wideband of interest. Also, we present a mathematical framework of the proposed algorithm and an expression for the detection threshold of the proposed detector is derived. The simulation results have showed a superior performance of the edge detection algorithm to different wavelet-based techniques at low-to-medium noise power. Used in conjunction with denoising, the proposed edge detector shows good detection results at low signal-to-noise ratio. For the primary user detection phase, we introduce the improved passband autocepstrum detector to tackle the misdetection problem of noise-like signals and it outperforms different state-of-the-art techniques. Finally, the uncertainty problem of the subbands center frequencies is addressed and the baseband autocepstrum detector is introduced as a potential solution to improve signal detection in frequency selective fading.

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Total variation denoising method to improve the detection process in IR images

Cited by 6 publications

References 22 publications

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

Deep learning based automated detection of intraretinal cystoid fluid

A Wideband Spectrum Sensing Approach for Cognitive Radios Based on Cepstral Analysis

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