Now showing items 1-18 of 18

      Authors Name
      Tahir, Mehwish [1]
      Tanseer, Faryal [1]
      Tanseer, Iffrah [1]
      Technological University of the Shannon (TUS), Ireland under President’s Doctoral Scholarship 2020, and Science Foundation Ireland (SFI) under Grant Number SFI 16/RC/3918, co funded by the European Regional Develop [1]
      Technological University of the Shannon Midlands Midwest [6]
      Technological University of the Shannon under the Staff Development Programme, and Science Foundation Ireland (SFI) under Grant Number SFI 16/RC/3918, co-funded by the European Regional Development Fund. R [1]
      Technological University of the Shannon: Midlands Midwest [5]
      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. [1]
      This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 847577; and a research grant from Science Foundation Ireland (SFI) under Grant Number 16/RC/3918 (Ireland's European Structural and Investment Funds Programmes and the European Regional Development Fund 2014-2020) . [1]
      This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 847577; and a research grant from Science Foundation Ireland (SFI) under Grant Number 16/RC/3918 (Ireland’s European Structural and Investment Funds Programmes and the European Regional Development Fund 2014-2020) [1]
      This project reported in this paper has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 700071 for the PROTECTIVE project. [1]
      This research is supported by Visvesvaraya Ph.D scheme under the Ministry of Electronics & Information Technology (MeitY) Government of India [1]
      This research was funded by grant number 16/RC/3918 and the APC was funded by Science Foundation Ireland [1]
      This research was supported by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918 (Confirm) and Marie Sklodowska Curie Grant agreement No. 847577 co-funded by the European Regional Development Fund. Wasif Afzal has received funding from the European Union’s Horizon 2020 research and innovation program under Grant agreement Nos. 871319, 957212; and from the ECSEL Joint Undertaking (JU) under Grant agreement No 101007350 [1]
      This work is in part supported by the Fundamental Research Funds for the Central Universities (B200202216) and in part supported by Innovation Foundation of Radiation Application, China Institute of Atomic Energy (KFZC2020010401). [1]
      This work was supported in part by Irish Research Council under ID GOIPD/2019/874, Science Foundation Ireland (SFI) under Grant Number 16/RC/3918 and 13/SIRG/2178, co-funded by the European Regional Development Fund, National Natural Science Foundation of China under Grant Number 61671126, Science and Technology Program of Sichuan Province of China under Grant Number 2019JDR0022 [1]
      This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme through the Marie Sklodowska-Curie under the Project MF-2018-0058 and Grant 713654, in part by the Science Foundation Ireland (SFI) under Grant SFI 16/RC/3918, and in part by the European Regional Development Fund. [1]
      This work was supported in part by the NCC Laboratory, Department of Electronics Engineering, IIT (BHU), India, under Grant IS/ST/EC-13-14/02 and I-DAPT HUB Foundation, IIT(BHU), India, under Grant R&D/SA/I-DAPT IIT(BHU)/ECE/21-22/02/290. The work of Saeed Hamood Alsamhi was supported in part by the European Union’s Horizon 2020 Research and Innovation Program under Marie Skłodowska-Curie Grant 847577, and in part by the Science Foundation Ireland (SFI) under Grant 16/RC/3918 (Ireland’s European Structural and Investment Funds Programmes and the European Regional Development Fund 2014–2020). The work of Faris A. Almalki was supported in part by the Deanship of Scientific Research at Taif University, Kingdom of Saudi Arabia for funding this project through Taif University Researchers Supporting Project Number (TURSP-2020/265). [1]