Leveraging Self-Organizing Maps for Effective Image Restoration
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Abstract
Computer vision relies critically on image restoration techniques to recreate clear images from damaged observations. Traditional methods face challenges when attempting to balance removing image noise and protecting image details. A novel framework that employs Self-Organizing Maps (SOMs) establishes a practical approach to restoring images will be investigated in this paper. Our restoration approach starts with image pre-processing, which feeds trained SOM features into a deep neural network to optimize outcome quality. This research evaluates our approach on benchmark datasets, achieving quantitative results: Our SOM-based method produces restoration outcomes with an average Peak Signal-to-Noise Ratio (PSNR) performance of 32.10 dB and Structural Similarity Index (SSIM) values of 0.894 that exceed state-of-the-art GAN-based restoration (31.75 dB, 0.890). According to this research, UTH can restore images by achieving enhanced clarity with preserved details. The successful merger between SOMs and deep learning architectures is our study's distinctive feature while creating opportunities for additional image processing applications.
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