Abstract
The constant development and the wider usage of computed tomography (CT) has gained the attention of researchers during medical practice related to radiation dose. The intention to eradicate the dosage causes increased artefacts and noise influences the confidentiality of the radiologist. Specifically, various noises like speckle, pepper, impulse, salt and Gaussian noise are determined as the complex source of image noise. Therefore, the enhancements in image reconstruction from the CT image need to be enhanced with the diagnostic performance are a challenging issue related to ill-posed nature. Various CT prediction approaches have given superior outcomes. Moreover, there are fewer works in image denoising using learning approaches as these approaches pretend to reduce mean square error (MSE) among the denoised CT images. The outcomes of the models are compromising and show the visibility of aggressive denoising. This research focuses on modelling an efficient approach for CT image denoising with an adaptive adversarial network model that uses weighted similarity measure (\(\mathrm{aa}-\mathrm{WSM}\)). The anticipated model has the key concept of enhancing the performance and reducing the error rate. The proposed model concentrates on migrating the noise distribution and learning the visual perception knowledge to perform the denoising task. The PSNR of the proposed model is 16.6 and SSIM is 0.64 which is comparatively better than existing methods. The suppressed noise is 5.2, artefacts reduction is 3.6 and the overall quality is 3.8 which is nominal than others. The model handles the essential information and manages the error rate with promising outcomes.
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Karthikram, A., Saravanan, M. An adaptive network model-based weighted similarity measure for CT image denoising. Soft Comput 28, 627–640 (2024). https://doi.org/10.1007/s00500-023-09399-9
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DOI: https://doi.org/10.1007/s00500-023-09399-9