Image noise reduction based on adaptive thresholding and clustering

被引:1
|
作者
Ali Abdullah Yahya
Jieqing Tan
Benyu Su
Kui Liu
Ali Naser Hadi
机构
[1] Anqing Normal University,School of Computer and Information
[2] Hefei University of Technology,School of Computer and Information
来源
关键词
Adaptive thresholding; Hard-thresholding; Soft-thresholding; K-means clustering; Block matching; Reference-blocks; Candidate-blocks;
D O I
暂无
中图分类号
学科分类号
摘要
In this paper, we present a novel image denoising method based on adaptive thresholding and k-means clustering. In this method, we adopt the adaptive thresholding technique as an alternative to the traditional hard-thresholding of the block-matching and 3D filtering (BM3D) method. This technique has a high capacity to adapt and change according to the amount of the noise. More precisely, in our method the soft-thresholding is applied to the areas with heavy noise, on the contrary the hard-thresholding is applied to the areas with slight noise. Based on the adaptation and stability of the adaptive thresholding, we can achieve optimal noise reduction and maintain the high spatial frequency detail (e.g. sharp edges). Owing to the capacity of k-means clustering in terms of finding the relevant candidate-blocks, we adopt this clustering at the last estimate to partition the denoised image into several regions and identify the boundaries between these regions. Applying k-means clustering will allow us to force the block matching to search within the region of the reference block, which in turn will lead to minimize the risk of finding poor matching. The main reason of applying the K-means clustering method on the denoised image and not on the noised image is specifically due to the flaw of accuracy in detecting edges in the noisy image. Experimental results demonstrate that the new algorithm consistently outperforms other reference methods in terms of visual quality, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Furthermore, in the proposed algorithm the time consumption of the image denoising is less than that in the other reference algorithms.
引用
收藏
页码:15545 / 15573
页数:28
相关论文
共 50 条
  • [1] Image noise reduction based on adaptive thresholding and clustering
    Yahya, Ali Abdullah
    Tan, Jieqing
    Su, Benyu
    Liu, Kui
    Hadi, Ali Naser
    MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (11) : 15545 - 15573
  • [2] CT Image Noise Reduction Based on Adaptive Wiener Filtering with Wavelet Packet Thresholding
    Diwakar, Manoj
    Kumar, Manoj
    2014 INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND GRID COMPUTING (PDGC), 2014, : 94 - 98
  • [3] Fluorescence microscopy image noise reduction using IEMD-based adaptive thresholding approach
    Rasal, Tushar
    Veerakumar, Thangaraj
    Subudhi, Badri Narayan
    Esakkirajan, Sankaralingam
    SIGNAL IMAGE AND VIDEO PROCESSING, 2023, 17 (01) : 237 - 245
  • [4] Fluorescence microscopy image noise reduction using IEMD-based adaptive thresholding approach
    Tushar Rasal
    Thangaraj Veerakumar
    Badri Narayan Subudhi
    Sankaralingam Esakkirajan
    Signal, Image and Video Processing, 2023, 17 : 237 - 245
  • [5] Thresholding neural network for adaptive noise reduction
    Zhang, XP
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 2001, 12 (03): : 567 - 584
  • [6] Adaptive document image thresholding using foreground and background clustering
    Savakis, AE
    1998 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING - PROCEEDINGS, VOL 3, 1998, : 785 - 789
  • [7] A novel video noise reduction method based on PDE, adaptive grouping, and thresholding techniques
    Yahya, Ali Abdullah
    Tan, Jieqing
    Hu, Min
    JOURNAL OF ENGINEERING-JOE, 2021, 2021 (10): : 605 - 620
  • [8] Space-scale adaptive noise reduction in images based on thresholding neural network
    Zhang, XP
    2001 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-VI, PROCEEDINGS: VOL I: SPEECH PROCESSING 1; VOL II: SPEECH PROCESSING 2 IND TECHNOL TRACK DESIGN & IMPLEMENTATION OF SIGNAL PROCESSING SYSTEMS NEURALNETWORKS FOR SIGNAL PROCESSING; VOL III: IMAGE & MULTIDIMENSIONAL SIGNAL PROCESSING MULTIMEDIA SIGNAL PROCESSING - VOL IV: SIGNAL PROCESSING FOR COMMUNICATIONS; VOL V: SIGNAL PROCESSING EDUCATION SENSOR ARRAY & MULTICHANNEL SIGNAL PROCESSING AUDIO & ELECTROACOUSTICS; VOL VI: SIGNAL PROCESSING THEORY & METHODS STUDENT FORUM, 2001, : 1889 - 1892
  • [9] Adaptive Wavelet Thresholding for Noise reduction in Electrocardiogram (ECG) Signals
    Kaur, Manpreet
    Kaur, Gagandeep
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2015, 15 (04): : 100 - 105
  • [10] Spatiotemporal Video Denoising Based on Adaptive Thresholding and Clustering
    Yahya, Ali Abdullah
    Tan, Jieqing
    Su, Benyu
    Liu, Kui
    Hadi, Ali Naser
    DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2017, 2017