en Enhancing the Quality of Melanoma Dermatoscopic Images Using Wavelet Coefficients within a Deep Learning Framework عمومى General پژوهشي Research <strong>Background:</strong> High-quality medical images improve accuracy in diagnosing diseases. Melanoma is a common and deadly skin cancer. Dermatoscopes offer a non-invasive way to capture skin lesions, but dermatoscopic image quality greatly affects diagnosis.<br> <strong>Methods:</strong> We propose a deep learning&ndash;based method to enhance dermatoscopic image quality using wavelet detail coefficients. ASuper-Resolution Convolutional Neural Network (SRCNN) estimates high-resolution wavelet coefficients from low-resolution images. This approach enables efficient training with fewer samples and lower computational cost. Refinement of these coefficients leads to better image reconstruction, measured using Peak Signal-to-Noise Ratio (PSNR).<br> <strong>Results:</strong> Tests on 180 dermatoscopic images show our method achieves a PSNR of 48.99. Previous approaches reached a maximum PSNR of 44.02.<br> <strong>Conclusion:</strong> This result shows our framework can provide high-quality images, supporting more accurate melanoma diagnosis. Melanoma, Deep Learning, Wavelet Transform, Dermatoscopic Imaging, Image Resolution Enhancement 48 55 http://icml.ir/browse.php?a_code=A-10-652-1&slc_lang=en&sid=1 Haleh Fateh هاله فاتح hfateh203@gmail.com 1050031947532846005514 1050031947532846005514 Yes Faculty of Electrical and Computer Engineering, K.N.Toosi University of Technology, Tehran, Iran Mohammad TashnehLab halefateh301@gmail.com 1050031947532846005515 1050031947532846005515 No Professor, Faculty of Electrical and Computer Engineering, K.N.Toosi University of Technology, Tehran, Iran Mansoor Fateh hfateh301@gmail.com 1050031947532846005516 1050031947532846005516 No Associate Professor, Faculty of Electrical and Computer Engineering, Shahroud University of Technology, Shahroud, Iran