Advancements in Image Processing Techniques Enhancing Image Quality and Recognition Accuracy

Abstract

This study addresses critical challenges in real-world image processing, including noise reduction, super-resolution, and recognition accuracy, by developing novel and scalable methodologies. Current approaches often excel in controlled environments but falter under real-world constraints, such as noise, low resolution, and limited computational resources. To overcome these limitations, this research introduces three key innovations: a custom Denoising Autoencoder (DAE) with residual connections and attention mechanisms for adaptive noise reduction, an enhanced Super-Resolution Generative Adversarial Network (SRGAN) leveraging multi-scale perceptual loss for superior detail preservation, and Vision Transformers (ViTs) for robust image recognition in constrained environments. Additionally, a novel evaluation metric, the Multi-Scale Perceptual Similarity Index (MS-PSI), is proposed to assess perceptual fidelity across resolutions, surpassing traditional metrics like SSIM. Experimental results reveal significant advancements, including a 16% improvement in PSNR, a 7.5% gain in SSIM, and a 3.6% boost in recognition accuracy across standard benchmarks. These techniques are optimized for deployment in resource-constrained environments using TensorRT and Quantized Neural Networks, enabling real-time applications. The proposed methods have transformative potential across diverse domains, from enhancing diagnostic precision in medical imaging to improving object recognition in autonomous systems and multimedia streaming. This work bridges theoretical innovation with practical solutions, establishing a robust foundation for future advancements in image processing.