en 4-Biomedical Signal & Image Processing Biomedical Signal & Image Processing Research Paper Research Paper <div style="text-align: justify;"><span style="color:#000000;"><span style="font-size:14px;"><span style="line-height:115%"><span new="" roman="" style="font-family:" times="">Speckle noise is an inherent artifact appearing in medical images that significantly lowers the quality and accuracy of diagnosis and treatment. Therefore, speckle reduction is considered as an essential step before processing and analyzing the ultrasound images. In this paper, we propose an ultrasound speckle reduction method based on speckle noise model estimation using a deep learning architecture called &ldquo;speckle noise-based inception convolutional denoising neural network&quot; (SNICDNN). Regarding the complicated nature of speckle noise, an inception module is added to the first layer to boost the power of feature extraction. Reconstruction of the despeckled image is performed by introducing a mathematical method based on solving a quadratic equation and applying an image-based inception convolutional denoising autoencoder (IICDAE). The results of various quantitative and qualitative evaluations on real ultrasound images demonstrate that SNICDNN outperforms the state-of-the-art methods for ultrasound despeckling. SNICDNN achieves 0.4579 dB and 0.0100 additional gains on average for PSNR and SSIM, respectively, compared to other methods. Denoising ultrasound based on its noise model estimation is not only a novel approach in comparison to traditional denoising autoencoder models but also due to the fact that it uses mathematical solutions to recover denoised images, SNICDNN shows a greater power in ultrasound despeckling. </span></span></span></span></div> Denoising Autoencoder, Inception Convolutional Neural Network, Speckle Noise Estimation, Ultrasound Image Denoising. 1 13 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-2421-2&slc_lang=en&sid=1 O. Mahmoudi Mehr Omid Mahmoudi Mehr omid_mahmoudimehr@alumni.iust.ac.ir 1800319475328460012739 1800319475328460012739 No School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran. M. R. Mohammadi Mohammad Reza Mohammadi mrmohammadi@iust.ac.ir 1800319475328460012740 1800319475328460012740 No School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran. M. Soryani Mohsen Soryani soryani@iust.ac.ir 1800319475328460012741 1800319475328460012741 Yes School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.