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23 results for “Image Denoising”
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Research ArticleOpen accessPhrase in the titleAn Adaptive Detail Preserving Modified Progressive Pca Thresholding Based Image DenoisingN. Anilkumar, S. Radhakrishnan, Shaik Syed Basha et al.Radiologists use medical imaging tools to assess the anatomy and function of interior organs. However, medical images are frequently distorted by various types of noise generated during the capture, transmission, and reconstruction operations, which can dramatically reduce image quality and hide critical diagnostic features. As a result, developing effective denoising algorithms that suppress noise while maintaining structural information remains a … …Journal of Computer Science, Vol. 22, Issue 8 · 8 Sep 2026 · 529 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA Detailed Preserving Medical Image Denoising Using Cluster-Wise PCA Thresholding and Iterative Mean FilteringMohit Sharma, Ayush Dogra, Anita Gupta et al.Image denoising is a vital process in medical imaging that involves removing noise or distortions introduced during image acquisition. Random noise can degrade image quality and reduce contrast, making accurate analysis more challenging. This paper presents a detail-preserving medical image denoising framework based on a hybrid methodology that combines cluster-wise Principal Component Analysis (PCA) thresholding with Iterative Mean Filtering (IMF). … …Journal of Computer Science, Vol. 22, Issue 4 · 18 Apr 2026 · 1,787 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA Comprehensive Review of Medical Image Denoising: Techniques, Challenges, Applications and Future DirectionsMohit Sharma, Ayush Dogra, Anita Gupta et al.The concept of noise reduction in medical images plays a vital role in improving image quality and accuracy associated with diagnosis, and facilitating reliable automated analysis. This survey presents a structured overview of medical image denoising, beginning with foundational definitions and noise models for modalities such as MRI and CT. We critically evaluate persistent gaps, including the limited generalizability of … …Journal of Computer Science, Vol. 22, Issue 3 · 28 Mar 2026 · 1,849 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleK-MEANS BASED IMAGE DENOISING USING BILATERAL FILTERING AND TOTAL VARIATIONDanu Wiroteurairuang, Sanun Srisuk, Chom Kimpan, Thanwa SripramongBilateral filter and Total variation image denoising are widely used in image denoising. In low noisy level, bilateral filtering is better than TV denoising for it reveals better SNR and sharper edges. However, in high noisy level, TV denoising outperforms bilateral filtering in terms of SNR and more details of non edges. It is very difficult to perform denoising of … …Journal of Computer Science, Vol. 10, Issue 12 · 30 Dec 2014 · 8,171 accessesView PDFCite
Review ArticleOpen accessPhrase in the titleUnleashing the Power of Image Denoising: A Comprehensive Review of Classical to Deep Learning MethodsArchana Saini, Ayush DograImage denoising is a vital step in many image processing and computer vision professions that aims to improve picture quality by decreasing noise while maintaining important image information. In this article, a detailed overview is presented of traditional and deep learning based picture denoising approaches. Classical methods, such as linear filtering, transform domain techniques, and patch-based approaches like Non-Local Means … …Journal of Computer Science, Vol. 21, Issue 8 · 8 Oct 2025 · 3,107 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleFUZZY SHRINK IMAGE DENOISING USING SMOOTHING SPLINE ESTIMATIONG.M. Rajathi, R. Rangarajan, R. Haripriya, R. NithyaThe image data is normally corrupted by additive noise during acquisition. This reduces the accuracy and reliability of any automatic analysis. For this reason, denoising methods are often applied to restore the original image. In proposed method a wavelet shrinkage algorithm based on fuzzy logic and the DT-DWT scheme is used. In particular, intra-scale dependency within wavelet coefficients is modeled … …Journal of Computer Science, Vol. 10, Issue 10 · 9 Jul 2014 · 6,681 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleModified Curvelet Thresholding Algorithm for Image DenoisingAl-Dahoud Ali, Preety D. Swami, J. SinghaiProblem statement: This study introduced an adaptive thresholding method for removing additive white Gaussian noise from digital images. Approach: Curvelet transform employed in the proposed scheme provides sparse decomposition as compared to the wavelet transform methods which being nongeometrical lack sparsity and fail to show optimal rate of convergence. Results: Different behaviors of curvelet transform maxima of image and noise … …Journal of Computer Science, Vol. 6, Issue 1 · 31 Jan 2010 · 7,255 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleEnhancing Brain MR Image Quality Using CNN With Best Denoising Modality for Improved Diagnosis of Abnormality: An AppraisalKavery Verma, Subodh Srivastava, Ritesh Kumar MishraDigital medical images acquired from the brain are highly susceptible to noise, which causes significant challenges for radiologists to identify abnormalities in a precise manner. Noise interference hampers both diagnostic accuracy and the interpretation of underlying abnormalities, potentially leading to flaw conclusions. Magnetic Resonance (MR) imaging is the most preferred digital imaging technique for brain abnormality detection. To achieve precise … …Journal of Computer Science, Vol. 22, Issue 3 · 26 Mar 2026 · 1,462 accessesView PDFCite
Research ArticleOpen accessIn the keywordsComparative Analysis of Wavelet, Curvelet, and Contourlet Transforms for Denoising Liver CT ImagesMajzoob Kamalaldin Omer AbdalrahmanLiver cancer is one of the leading causes of mortality worldwide, and early detection using CT imaging plays a critical role in patient survival. However, the quality of CT scans is often degraded by noise, particularly in low-dose imaging intended to minimize radiation exposure. This study presents a comprehensive comparison of three multiresolution transform techniques, namely the wavelet, curvelet, and … …Journal of Computer Science, Vol. 22, Issue 1 · 4 Feb 2026 · 2,117 accessesView PDFCite
Research ArticleOpen accessIn the keywordsRegularization Method for Solving Denoising and Inpainting Task Using Stacked Sparse Denoising AutoencodersPavel Vyacheslavovich Skribtsov, Sergey Olegovich SurikovThis article offers a regularization method for training stacked sparse denoising autoencoders aimed at designing model description of objects used for image denoising and inpainting. The offered regularization method allows increasing the generalizing ability of model description, which results in greater stability of denoising methods using it with regard to variation of the noise type. This makes the offered method … …American Journal of Applied Sciences, Vol. 13, Issue 1 · 13 Jan 2016 · 7,609 accessesView PDFCite