Akademik Veri Yönetim Sistemi
KSII Transactions on Internet and Information Systems, cilt.19, sa.3, ss.704-729, 2025 (SCI-Expanded)
Video and image denoising techniques aim to eliminate noise while preserving image details. However, in the process of noise reduction, while some image texture may get lost, residual noise artifacts can persist. So far, no single model has achieved universal success across all types and levels of noise. In this study, we present a noise reduction model that combines deep learning methods using limited hardware resources. Specifically, we leverage a combination of Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) architecture, which has demonstrated effectiveness in preserving image structure as the depth of the model increases. Additionally, we enhance visual quality by incorporating GAN model after the CNN network. Our evaluation of the proposed approach reveals superior performance at low noise levels compared to previous neural network-based methods. By utilizing the findings of this study, we gain insights into the more efficient utilization of deep learning and traditional noise reduction methods, explore their behavior at different noise levels, and compare results obtained from diverse datasets. The proposed method outperforms other CNN-based methods at certain noise levels, thereby providing valuable prior knowledge to researchers in this field.