Image Super Resolution - Methods and Evaluation: Exploring image super-resolution methods for enhancing image resolution and quality using deep learning and traditional techniques
Keywords:
Image Super-Resolution, Deep LearningAbstract
Image super-resolution (SR) is a critical task in computer vision aimed at enhancing the resolution and quality of images. This paper provides a comprehensive review of image super-resolution methods, focusing on both traditional and deep learning-based techniques. The paper discusses the challenges associated with image super-resolution and evaluates the performance of different methods using standard evaluation metrics. The findings suggest that deep learning-based approaches have shown remarkable advancements in image super-resolution, outperforming traditional methods in terms of both quantitative metrics and visual quality. However, challenges such as computational complexity and generalization to diverse image datasets remain. This paper concludes with recommendations for future research directions in the field of image super-resolution.
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