Advances in Digital Signal Processing for Real-Time Image Enhancement Algorithms

Abstract

The rapid growth of digital imaging systems in applications such as medical imaging, surveillance, autonomous vehicles, and multimedia has created a strong demand for efficient real-time image enhancement techniques. Digital Signal Processing (DSP) provides a robust framework for improving image quality through noise reduction, contrast enhancement, and feature extraction. This research paper presents a comprehensive theoretical and analytical study of advances in DSP-based real-time image enhancement algorithms. The study explores classical techniques such as spatial and frequency domain filtering alongside modern approaches including adaptive filtering, transform-based methods, and deep learning-assisted DSP models. The results demonstrate that hybrid and real-time optimized algorithms significantly improve image quality while maintaining computational efficiency. The paper concludes with future directions emphasizing AI integration and hardware acceleration.