Researchers improve chaotic mapping for super-resolution image reconstruction

Super-resolution (SR) technology plays a pivotal role in enhancing the quality of images. SR reconstruction aims to generate high-resolution images from low-resolution ones. Traditional methods often result in blurred or distorted images. Advanced techniques such as sparse representation and deep learning-based methods have shown promising results but still face limitations in terms of noise robustness and computational complexity.