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Journal articles on the topic 'Hybrid denoising'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Jebur, Rusul Sabah, Mohd Hazli Bin Mohamed Zabil, Dalal Abdulmohsin Hammood, Lim Kok Cheng, and Ali Al-Naji. "Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm." Technologies 11, no. 4 (2023): 111. http://dx.doi.org/10.3390/technologies11040111.

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Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising. Leveraging Bidirectional Long Short-Term Memory (Bi-LSTM) and optimized Convolutional Neural Networks (CNN), the hybrid model aims to enhance denoising performance. The CNN’s weights are optimized using SI-OPA, resulting in improved denoising accuracy. Extensive comparisons against state-of

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Ali, Hanan. "Hybrid Algorithm for Image Denoising." AL-Rafidain Journal of Computer Sciences and Mathematics 5, no. 1 (2008): 43–60. http://dx.doi.org/10.33899/csmj.2008.163961.

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3

Om, Hari, and Mantosh Biswas. "A Hybrid Image Denoising Method." International Journal of Computer Applications 58, no. 3 (2012): 21–26. http://dx.doi.org/10.5120/9262-3439.

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4

Jiang, S., and X. Hao. "Hybrid Fourier-wavelet image denoising." Electronics Letters 43, no. 20 (2007): 1081. http://dx.doi.org/10.1049/el:20071417.

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Zhang, Xiaobo, and Xiangchu Feng. "Hybrid gradient-domain image denoising." AEU - International Journal of Electronics and Communications 68, no. 3 (2014): 179–85. http://dx.doi.org/10.1016/j.aeue.2013.08.009.

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6

Mahmoud, Walid, and Raghad Jassim. "Image Denoising Using Hybrid Transforms." Engineering and Technology Journal 25, no. 5 (2007): 669–82. http://dx.doi.org/10.30684/etj.25.5.7.

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7

Sultan, Nora Hussam. "HYBRID IMAGE DENOISING USING WIENER FILTER WITH DISCRETE WAVELET TRANSFORM AND FRAMELET TRANSFORM." Kufa Journal of Engineering 7, no. 2 (2016): 122–33. http://dx.doi.org/10.30572/2018/kje/721211.

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Removal of noise from an image is an essential part of image processing systems. In this paper a hybrid denoising algorithm which combines spatial domain Wiener filter and thresholding function in the wavelet and framelet domain is done. In this work three algorithms are proposed. The first hybrid denoising algorithm using Wiener filter with 2-level discrete wavelet transform (DWT), the second algorithm its using Wiener filter with 2-level framelet transform (FLT) and the third hybrid denoising algorithm its combines wiener filter with 1-level wavelet transform then apply framelet transform on

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Bian, Shengqin, Xinyu He, Zhengguang Xu, and Lixin Zhang. "Hybrid Dilated Convolution with Attention Mechanisms for Image Denoising." Electronics 12, no. 18 (2023): 3770. http://dx.doi.org/10.3390/electronics12183770.

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In the field of image denoising, convolutional neural networks (CNNs) have become increasingly popular due to their ability to learn effective feature representations from large amounts of data. In the field of image denoising, CNNs are widely used to improve performance. However, increasing network depth can weaken the influence of shallow layers on deep layers, especially for complex denoising tasks such as real denoising and blind denoising, where conventional networks fail to achieve high-quality results. To address this issue, this paper proposes a hybrid dilated convolution-based denoisi

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jun Wang, Hai, Menke Neimule, and Jin Tao. "Application of HS-LMBP Hybrid Neural Network Algorithm in Image Denoising." MATEC Web of Conferences 173 (2018): 03056. http://dx.doi.org/10.1051/matecconf/201817303056.

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In order to overcome the problems such as poor global search ability, slow convergence rate, and easy to fall into local minimum values in the image denoising process of traditional BP neural networks, the HS-LMBP hybrid neural network image denoising algorithm is proposed which combines the harmony search algorithm and the LMBP algorithm. The HS-LMBP hybrid neural network algorithm combines the high speed of the LMBP algorithm and the global nature of the HS algorithm, which can be a good improvement to the existing problems of the BP algorithm model. Compared with the Wiener filtering, BP, L

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Huang, Xin, Weiwei Qian, Peng Zhang, Zhongtian Ding, and Shunming Li. "A hybrid transformer masked time-domain denoising network for vibration signals." Measurement Science and Technology 36, no. 1 (2024): 016193. https://doi.org/10.1088/1361-6501/ad99f2.

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Abstract Health-condition-sensitive vibration information is prone to be swamped by widespread noise. Denoising is always indispensable, but existing methods still lack adaptability. Therefore, a novel intelligent denoising framework called a hybrid transformer masked time-domain denoising network (HTMTDN) is proposed. First, a dense dilation convolution block and a hybrid transformer are constructed to deal with fault impulse scale variations and unexpected noise frequency bands respectively, which greatly improves the adaptive denoising capability and relieves tough denoising parameter tunin

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Ji, Xiaoping, Dazhi Zhang, Zhichang Guo, and Boying Wu. "Image Denoising via Nonlinear Hybrid Diffusion." Mathematical Problems in Engineering 2013 (2013): 1–22. http://dx.doi.org/10.1155/2013/890157.

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A nonlinear anisotropic hybrid diffusion equation is discussed for image denoising, which is a combination of mean curvature smoothing and Gaussian heat diffusion. First, we propose a new edge detection indicator, that is, the diffusivity function. Based on this diffusivity function, the new diffusion is nonlinear anisotropic and forward-backward. Unlike the Perona-Malik (PM) diffusion, the new forward-backward diffusion is adjustable and under control. Then, the existence, uniqueness, and long-time behavior of the new regularization equation of the model are established. Finally, using the ex

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Zhong, H., P. P. Han, X. H. Zhang, and Y. Q. Yu. "Hybrid patch similarity for image denoising." Electronics Letters 48, no. 4 (2012): 212. http://dx.doi.org/10.1049/el.2011.3580.

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13

Moe, Akari, and Soomin Jeon. "Hybrid Approach for Medical Image Denoising." Journal of information and communication convergence engineering 23, no. 1 (2025): 55–63. https://doi.org/10.56977/jicce.2025.23.1.55.

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14

Seetharaman, R., M. Tharun, and K. Anandan. "A Novel approach in Hybrid Median Filtering for Denoising Medical images." IOP Conference Series: Materials Science and Engineering 1187, no. 1 (2021): 012028. http://dx.doi.org/10.1088/1757-899x/1187/1/012028.

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Abstract Image denoising is a key pre-processing step in medical image analysis. At current, deep learning-based models have shown a great promise, which outperformed many conventional methods over the past three decades. Speckle noise removal is a major issue in preserving all the delicate details and the edges in ultrasound image processing as it degrades the visual evaluation of ultrasound images. The multiplicative behavior of speckle-noise is converted into additive by using log transform as the additive noise removal is easy as compared to multiplicative noise. An innovative approach for

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Choubey, Shruti Bhargava, and S. P. V. Subba Rao. "Implementation of hybrid filter technique for noise removal from medical images." International Journal of Engineering & Technology 7, no. 1.1 (2017): 25. http://dx.doi.org/10.14419/ijet.v7i1.1.8917.

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Image denoising is used to eliminate the noise while retaining as much as possible the important signal features. The function of image denoising is to calculate approximately the original image form the noisy data. Image denoising still remains the challenge for researchers because noise removal introduces artifacts and causes blurring of the images. Image denoising has become an essential exercise in medical imaging especially the Magnetic Resonance Imaging (MRI). MR images are typically corrupted with noise, which hinder the medical diagnosis based on these images. The presence of noise not

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Zhou, Yongjun, Huiliang Cao, and Tao Guo. "A Hybrid Algorithm for Noise Suppression of MEMS Accelerometer Based on the Improved VMD and TFPF." Micromachines 13, no. 6 (2022): 891. http://dx.doi.org/10.3390/mi13060891.

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High-G MEMS accelerometer (HGMA) is a new type of sensor; it has been widely used in high precision measurement and control fields. Inevitably, the accelerometer output signal contains random noise caused by the accelerometer itself, the hardware circuit and other aspects. In order to denoise the HGMA’s output signal to improve the measurement accuracy, the improved VMD and TFPF hybrid denoising algorithm is proposed, which combines variational modal decomposition (VMD) and time-frequency peak filtering (TFPF). Firstly, VMD was optimized by the multi-objective particle swarm optimization (MOPS

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Alaoui, Nail, Amel Baha Houda Adamou-Mitiche, Lahcène Mitiche, and Lakhdar Bouhamla. "Image Denoising Based on Improved Hybrid Genetic Algorithm." Review of Computer Engineering Studies 8, no. 1 (2021): 14–21. http://dx.doi.org/10.18280/rces.080103.

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Digital images can be degraded through noise during the transmission and process of acquisition, it is still a fundamental challenge is to eliminate as much noise as possible while preserving the main features of the image, for instance, edges, texture, and corners. This paper proposes for image denoising a new Improved Hybrid Genetic Algorithm (IHGA), whose combined a Genetic Algorithm (GA), with some image denoising methods. Wherein this approach uses mutation operators, crossover, and population reinitialization as default operators available in evolutionary methods with applied some state-

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Alaoui, Nail, Amel Baha Houda Adamou-Mitiche, and Lahcène Mitiche. "Image Denoising Based on Improved Hybrid Genetic Algorithm." Advanced Science, Engineering and Medicine 12, no. 12 (2020): 1560–68. http://dx.doi.org/10.1166/asem.2020.2675.

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Digital images can be degraded through noise during the transmission and process of acquisition, it is still a fundamental challenge is to eliminate as much noise as possible while preserving the main features of the image, for instance, edges, texture, and corners. This paper proposes for image denoising a new Improved Hybrid Genetic Algorithm (IHGA), whose combined a Genetic Algorithm (GA), with some image denoising methods. Wherein this approach uses mutation operators, crossover, and population reinitialization as default operators available in evolutionary methods with applied some state-

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Verma, Atul Kumar, Barjinder Singh Saini, and Taranjit Kaur. "Image Denoising using Alexander Fractional Hybrid Filter." International Journal of Image and Graphics 18, no. 01 (2018): 1850003. http://dx.doi.org/10.1142/s0219467818500031.

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In this paper, a hybrid filter based on the concept of fractional calculus and Alexander polynomial is proposed. The hybrid filtering mask is constructed by convolving the designed Alexander fractional differential and integral masks. The hybrid mask shows high robustness for images corrupted with Gaussian, salt & pepper, and speckle noises. For the experimentation, the standard and real world noisy images are used. The qualitative comparison shows that the proposed hybrid filter has better denoising with high edge preserving capability as compared to the other existing filters. Quantitati

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He, Sun, and Wang. "Noise Reduction for MEMS Gyroscope Signal: A Novel Method Combining ACMP with Adaptive Multiscale SG Filter Based on AMA." Sensors 19, no. 20 (2019): 4382. http://dx.doi.org/10.3390/s19204382.

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In this paper, a novel hybrid method combining adaptive chirp mode pursuit (ACMP) with an adaptive multiscale Savitzky–Golay filter (AMSGF) based on adaptive moving average (AMA) is proposed for offline denoising micro-electromechanical system (MEMS) gyroscope signal. The denoising scheme includes preliminary denoising and further denoising. At the preliminary denoising stage, the original gyroscope signal is decomposed into signal modes one by one using ACMP with modified stopping criterion based on mutual information. Useful information is extracted while most noise is discarded in the resid

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21

Hong, Hoonbin, and Ming Liang. "K-Hybrid: A Kurtosis-Based Hybrid Thresholding Method for Mechanical Signal Denoising." Journal of Vibration and Acoustics 129, no. 4 (2007): 458–70. http://dx.doi.org/10.1115/1.2748467.

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This paper presents a kurtosis-based hybrid thresholding method, K-hybrid, for denoising mechanical fault signals. The threshold used in the hybrid thresholding method is determined based on kurtosis, which is an important indicator of the signal-to-noise ratio (SNR) of a signal. This together with its sensitivity to outliers and data-driven nature makes a kurtosis-based threshold particularly suitable for on-line detection of mechanical faults featuring impulsive signals. To better reflect the signal composition, the proposed hybrid thresholding rule divides the wavelet transformed input sign

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Phaneendra, K., B. Tirupathi Rao, B. Nikhila Sree, J. Haswanth Kumar, and B. Harshavardhan Raju. "Novel Approach for MRI Image Quality Analysis by Hybrid Preprocessing Techniques." International Journal for Modern Trends in Science and Technology 6, no. 5 (2020): 27–32. http://dx.doi.org/10.46501/ijmtst060505.

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Image pre-preparing methods are utilized to improve the nature of a image before handling into an application. This uses a little neighborhood of a pixel in an information image to get another splendor esteem in the yield image. These pre-preparing methods are likewise called as filtration and goals upgrade. The clinical image quality parameters are fundamentally clamor and goals. The fundamental goal of this paper is to improve the image quality by denoising and goals upgrade. A large portion of the imaging methods are corrupted by clamor. So as to protect the edges and form data of the clini

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Zhang, Yunfeng, Jing Wang, and Yong Tian. "Hybrid image denoising based on region division." International Journal of Computer Applications in Technology 64, no. 3 (2020): 308. http://dx.doi.org/10.1504/ijcat.2020.10034161.

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Tian, Yong, Jing Wang, and Yunfeng Zhang. "Hybrid image denoising based on region division." International Journal of Computer Applications in Technology 64, no. 3 (2020): 308. http://dx.doi.org/10.1504/ijcat.2020.111843.

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Kim, D., H. S. Oh, and P. Naveau. "Hybrid wavelet denoising procedure of discontinuous surfaces." IET Image Processing 5, no. 8 (2011): 684. http://dx.doi.org/10.1049/iet-ipr.2010.0231.

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Hareesh, S., and S. Rethinavalli. "Bone Fracture Image Denoising using a Novel Filter with Hybrid Optimization." Indian Journal Of Science And Technology 18, no. 21 (2025): 1636–50. https://doi.org/10.17485/ijst/v18i21.899.

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Objective: The prime objective of this particular research is to enhance the quality of Bone fracture medical images by noise removal in the preprocessing stage, ensuring much better detection and analysis of anomalies in the human body. At the same time, it presents a solution to some of the deficits of traditional digital filters with regard to the optimization robustness and time efficiency. Method: Introduces a Decimation Based Guided Box Filter novelty used in the first time for Bone fracture Medical Image Denoising. For performance optimization, a hybrid algorithm based on the Particle S

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Geetika Agotra and Prof. Manish Kumar Singhal. "A Review of Image Denoising Using Fuzzy and Wiener Filters in the Wavelet Domain." International Journal of Scientific Research in Science and Technology 11, no. 5 (2024): 143–49. http://dx.doi.org/10.32628/ijsrst2411430.

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This paper focuses on image denoising using fuzzy wavelet domain transforms, reviewing recent advancements in this area. Wavelet transforms have become a powerful tool in image denoising, with one of the most widely used techniques involving thresholding wavelet coefficients. The paper proposes a hybrid denoising method that combines the wavelet transform, median filtering, and nonlinear diffusion. Additionally, a novel fuzzy filter is introduced to reduce additive noise in digital color images. Two distinct image denoising techniques are discussed: the first employs an Asymmetrical Triangular

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Gondal, Rashid Mehmood, Saima Anwar Lashari, Murtaja Ali Saare, and Sari Ali Sari. "A hybrid de-noising method for mammogram images." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (2021): 1435–43. https://doi.org/10.11591/ijeecs.v21.i3.pp1435-1443.

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In general, mammogram images are contaminated with noise which directly affects image quality. Several methods have been proposed to de-noise these images, however, there is always a risk of losing valuable information. In order to overcome the loss of information, the present study proposed a Hybrid denoising method for mammogram images. The proposed hybrid method works in two steps: Firstly, preprocessing with mathematical morphology was applied for image enhancement. Secondly, a global unsymmetrical trimmed median filter (GUTM) is applied to a de-noise image. Experimental results prove that

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Spriha Deshpande and Suman Basavaraj Mukkanagoudar. "From Noise to Clarity: A Hybrid Approach for Image Denoising Using Traditional and Deep Learning Methods." Journal of Computer Science and Technology Studies 2, no. 2 (2020): 39–52. https://doi.org/10.32996/jcsts.2020.2.2.5.

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In this study, we explore various image denoising techniques to restore images affected by noise, with a particular focus on traditional and deep learning-based methods. The research compares conventional denoising approaches, including Wavelet Thresholding, Bilateral Filtering, Non-Local Means, and Wavelet Denoising with Bayesian Shrinkage, against state-of-the-art deep learning models, such as DnCNN and U-Net. The performance of these methods is evaluated based on two metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Additionally, we investigate the potential

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Yang, Wuyi, Wenlei Chang, Zhongchang Song, Fuqiang Niu, Xianyan Wang, and Yu Zhang. "Denoising odontocete echolocation clicks using a hybrid model with convolutional neural network and long short-term memory network." Journal of the Acoustical Society of America 154, no. 2 (2023): 938–47. http://dx.doi.org/10.1121/10.0020560.

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Ocean noise negatively influences the recording of odontocete echolocation clicks. In this study, a hybrid model based on the convolutional neural network (CNN) and long short-term memory (LSTM) network—called a hybrid CNN-LSTM model—was proposed to denoise echolocation clicks. To learn the model parameters, the echolocation clicks were partially corrupted by adding ocean noise, and the model was trained to recover the original echolocation clicks. It can be difficult to collect large numbers of echolocation clicks free of ambient sea noise for training networks. Data augmentation and transfer

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Liu, Xiaodan, Li Wang, and Xueru Bai. "End-to-End Radar HRRP Target Recognition Based on Integrated Denoising and Recognition Network." Remote Sensing 14, no. 20 (2022): 5254. http://dx.doi.org/10.3390/rs14205254.

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For high-resolution range profile (HRRP) radar target recognition in a low signal-to-noise ratio (SNR) scenario, traditional methods frequently perform denoising and recognition separately. In addition, they assume equivalent contributions of the target and the noise regions during feature extraction and fail to capture the global dependency. To tackle these issues, an integrated denoising and recognition network, namely, IDR-Net, is proposed. The IDR-Net achieves denoising through the denoising module after adversarial training, and learns the global relationship of the generated HRRP sequenc

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Mehmood Gondal, Rashid, Saima Anwar Lashari, Murtaja Ali Saare, and Sari Ali Sari. "A hybrid de-noising method for mammogram images." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (2021): 1435. http://dx.doi.org/10.11591/ijeecs.v21.i3.pp1435-1443.

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<span id="docs-internal-guid-0cd3701c-7fff-a024-86c4-cd67dde5c463"><span>In general, mammogram images contaminated with noise which directly affect images quality. Several methods have been proposed to de-noise these images, however, there is always a risk of losing valuable information. In order to overcome the loss of information, the present study proposed a hybrid denoising method for mammogram images. The proposed hybrid method works in two steps: Firstly, preprocessing with mathematical morphology was applied for image enhancement. Secondly, global unsymmetrical trimmed media

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33

Hu, Yu Ping. "A Mixed Denoising Algorithm Based on Weighted Joint Sparse Representation." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 2 (2019): 313–16. http://dx.doi.org/10.20965/jaciii.2019.p0313.

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Joint sparse representation is not ideal for the processing of outliers in image, so a weighted joint sparse representation model for image denoising is proposed. This model introduces a weighted matrix of common information shared by data samples and reduces the influence of outliers. The greedy algorithm based on weighted simultaneous orthogonal matching pursuit is used to approximate the global optimal solution of the model effectively. The weighted noisy image block is used to remove the mixed noise of the image by jointly coding the nonlocal similar image blocks. By combining global prior

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An, Yang, Hak Keung Lam, and Sai Ho Ling. "Auto-Denoising for EEG Signals Using Generative Adversarial Network." Sensors 22, no. 5 (2022): 1750. http://dx.doi.org/10.3390/s22051750.

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The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function is defined to ensure that the filtered signal can retain as much effective original information and energy as possible. This model can imitate and integrate artificial denoising methods, which reduces processing time; hence it can be used for a large amount of data processing. Compared to other neu

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Kushwaha, Sumit, and Rabindra Kumar Singh. "Optimization of the proposed hybrid denoising technique to overcome over-filtering issue." Biomedical Engineering / Biomedizinische Technik 64, no. 5 (2019): 601–18. http://dx.doi.org/10.1515/bmt-2018-0101.

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Abstract Image denoising has become a crucial task in medical ultrasound (US) imaging due to the presence of speckle or multiplicative noise and additive Gaussian noise. Recently, several denoising techniques such as adaptive wavelet thresholding & joint bilateral (AWT + JB) filter, adaptive fuzzy switching weighted mean (AFSWM) filter and median patch-based locally optimal Wiener (MPBLOW) filter have been proposed to remove the speckle noise. However, these denoising techniques were found to remove noise along with the essential parts of the actual image data which is known as over-filter

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KHOUDOUR, Sofiane, Zoubeida MESSALI, and Rima BELKHITER. "Quantitative Comparative Study of Video Denoising with Optical Flow Estimation in Spatial and Transform Domains." Algerian Journal of Signals and Systems 5, no. 3 (2020): 159–66. http://dx.doi.org/10.51485/ajss.v5i3.112.

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In this paper we establish an extensive quantitative comparative study of patch-based video denoising with optical flow estimation algorithms. Namely, SPTWO, VBM3D and VBM4D algorithms are considered. The aim of this study is to combine these video denoising algorithms in a hybrid proposed process to take advantage of. SPTWO takes advantage of the self-similarity and redundancy of adjacent frames. The proposed hybrid algorithm and the three video denoising algorithms are implemented and tested on real sequences degraded by various level Additive White Gaussian Noise (AWGN). The obtained result

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Zhu, Jianguang, Ying Wei, Juan Wei, and Binbin Hao. "A Non-Convex Hybrid Overlapping Group Sparsity Model with Hyper-Laplacian Prior for Multiplicative Noise." Fractal and Fractional 7, no. 4 (2023): 336. http://dx.doi.org/10.3390/fractalfract7040336.

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Multiplicative noise removal is a quite challenging problem in image denoising. In recent years, hyper-Laplacian prior information has been successfully introduced in the image denoising problem and significant denoising effects have been achieved. In this paper, we propose a new hybrid regularizer model for removing multiplicative noise. The proposed model consists of the non-convex higher-order total variation and overlapping group sparsity on a hyper-Laplacian prior regularizer. It combines the advantages of the non-convex regularization and the hybrid regularization, which may simultaneous

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Prasad, Preena, Valer Niminet, and Anitha J. "A Hybrid Approach for CT-MR Brain Image Denoising Using PSO-Optimised Non-Local Means and Wiener Filtering." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 4 (2024): 87. https://doi.org/10.70594/brain/15.4/7.

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<p dir="ltr"><span>Noise in CT-MR brain images poses a critical challenge, significantly impacting diagnostic accuracy as well as clinical decision-making. Current medical image-denoising techniques struggle to effectively remove noise while preserving crucial image features. The hybrid technique’s potential that combines complementary denoising algorithms is highlighted by the limitations of these approaches. This paper develops &amp; evaluates a denoising method that combines the strengths of Particle Swarm optimised, Non-Local Means (NLM) &amp; Wiener filtering. The prop

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NagaPrudhviRaj, V., and T. Venkateswarlu. "Ultrasound Medical Image denoising using Hybrid Bilateral filtering." International Journal of Computer Applications 56, no. 14 (2012): 44–51. http://dx.doi.org/10.5120/8963-3171.

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Zhang, Fan, Hui Fan, Peiqiang Liu, and Jinjiang Li. "Image Denoising Using Hybrid Singular Value Thresholding Operators." IEEE Access 8 (2020): 8157–65. http://dx.doi.org/10.1109/access.2020.2964683.

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Li, Chengbo, Yu Zhang, and Charles C. Mosher. "A hybrid learning-based framework for seismic denoising." Leading Edge 38, no. 7 (2019): 542–49. http://dx.doi.org/10.1190/tle38070542.1.

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Noise attenuation has been a long-standing problem in seismic data processing. It presents unique challenges on land due to a complex near surface coupled with unavoidable environmental noise sources. In many cases, weak signals are embedded in much stronger noise, which makes conventional methods less effective at extracting those signals. In addition, conventional methods may lack adaptability to various noise types and patterns. Machine learning has shown great promise in solving geophysical problems including seismic data processing and interpretation. Here, we propose a novel method that

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Oh, Seungmi, Hyenkyun Woo, Sangwoon Yun, and Myungjoo Kang. "Non-convex hybrid total variation for image denoising." Journal of Visual Communication and Image Representation 24, no. 3 (2013): 332–44. http://dx.doi.org/10.1016/j.jvcir.2013.01.010.

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Rajan, Jeny, K. Kannan, and M. R. Kaimal. "An Improved Hybrid Model for Molecular Image Denoising." Journal of Mathematical Imaging and Vision 31, no. 1 (2008): 73–79. http://dx.doi.org/10.1007/s10851-008-0067-4.

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Mohamadi, Neda, Ali R. Soheili, and Faezeh Toutounian. "A new hybrid denoising model based on PDEs." Multimedia Tools and Applications 77, no. 10 (2017): 12057–72. http://dx.doi.org/10.1007/s11042-017-4858-8.

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Alam, Khursheed, Santosh Kumar, Nitendra Kumar, Shri Prakash Pandey, and Surya Kant Pal. "A Nonlinear Hybrid Diffusion Model for Image Denoising." Macromolecular Symposia 407, no. 1 (2023): 2100511. http://dx.doi.org/10.1002/masy.202100511.

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Prajapati, Ramkhelavan, and Agya Mishra. "DESIGN OF HYBRID ADAPTIVE MODEL FOR IMAGE DENOISING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27371.

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This thesis work proposes a new denoising algorithm based on Particle filter and Wavelet (Curvelet) transform combination, particle filter generates weights through SIR algorithm to cancel the interference of noise present in the image, while curvelet transform is used to shrink the remaining segments of noise, so this method can both remove image blurr and maintain good texture as well. The PF+Clet Image Denoiser is successfully designed and implemented, which is a new approach in image enhancement and Interference cancellation. This thesis concludes that it is quite efficient algorithm among

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Ding, Li, Haoran Sun, Haoliang Chen, and Xinyu Hu. "Hybrid multi-resolution network for DAS data denoising." PLOS One 20, no. 6 (2025): e0325299. https://doi.org/10.1371/journal.pone.0325299.

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The rapid advancement of Distributed Acoustic Sensing (DAS) technology has opened up extensive prospects within the field of seismic exploration. However, unforeseeable noise present in actual DAS seismic records has led to the submergence of valuable information beneath intense noise, significantly disrupting reflective signals and diminishing the signal-to-noise ratio (SNR) of seismic data. Consequently, subsequent processing, such as migration and imaging, and interpretation tasks are hindered. In pursuit of an effective denoising approach for DAS data, this study proposes a Hybrid Multi-Re

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Zheng, Zhiting, Shuqi Wu, and Wen Ding. "CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstruction." Applied and Computational Engineering 151, no. 1 (2025): 192–98. https://doi.org/10.54254/2755-2721/2025.23290.

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Low-dose CT (LDCT) images are often accompanied by significant noise, which negatively impacts image quality and subsequent diagnostic accuracy. To address the challenges of multi-scale feature fusion and diverse noise distribution patterns in LDCT denoising, this paper introduces an innovative model, CTLformer, which combines convolutional structures with transformer architecture. Two key innovations are proposed: a multi-scale attention mechanism and a dynamic attention control mechanism. The multi-scale attention mechanism, implemented through the Token2Token mechanism and self-attention in

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Huo, Chunling, Da Zhang, and Huanyu Yang. "An Underwater Image Denoising Method Based on High-Frequency Abrupt Signal Separation and Hybrid Attention Mechanism." Sensors 24, no. 14 (2024): 4578. http://dx.doi.org/10.3390/s24144578.

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During underwater image processing, image quality is affected by the absorption and scattering of light in water, thus causing problems such as blurring and noise. As a result, poor image quality is unavoidable. To achieve overall satisfying research results, underwater image denoising is vital. This paper presents an underwater image denoising method, named HHDNet, designed to address noise issues arising from environmental interference and technical limitations during underwater robot photography. The method leverages a dual-branch network architecture to handle both high and low frequencies

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Ma, Jun, Jiande Wu, and Xiaodong Wang. "A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing." Journal of Low Frequency Noise, Vibration and Active Control 37, no. 4 (2018): 928–54. http://dx.doi.org/10.1177/1461348418765973.

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Rolling bearing is one of the most crucial components in rotating machinery and due to their critical role, it is of great importance to monitor their operation conditions. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. Therefore, signal denoising preprocessing has become an essential part of condition monitoring and fault diagnosis. In the present study, a hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing is proposed. First, as a denoising prepro

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