Deep Learning-Driven Framework for Intelligent Image Processing and Feature Enhancement
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Abstract
Image quality enhancement remains a fundamental challenge in computer vision, particularly in applications such as super-resolution, denoising, and feature refinement, where preserving structural fidelity while improving perceptual realism is crucial. Traditional approaches, including conventional GAN-based models, have achieved notable progress but often face issues like texture inconsistencies, artifacts, and limited capability in modeling long-range dependencies. To overcome these limitations, this study proposes a Generative Adversarial Networks (GAN)-driven framework for intelligent image processing and feature enhancement. The framework leverages a combination of advanced GAN architectures and attention-based mechanisms to effectively capture both local and global contextual features, thereby enhancing image quality, detail preservation, and noise suppression. The proposed model is extensively evaluated on benchmark datasets such as DIV2K, Set5, and Urban100 using both full-reference metrics (PSNR, SSIM) and perceptual quality measures (LPIPS, NIQE). Experimental results demonstrate that the GAN-driven framework significantly outperforms existing CNN and GAN-based methods, providing superior reconstruction quality and robust generalization to real-world degraded images. This research contributes to the development of scalable and high-performance GAN-based solutions for intelligent image enhancement, with potential applications in medical imaging, satellite imagery, digital photography, and other high-fidelity imaging tasks.
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References
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