CSMambaDataSets

This guide provides instructions for utilizing benchmark datasets, including Set5, Set11, Set14, BSD100, BSD500, McM18, and Urban100, commonly employed in computer vision tasks such as super-resolution, denoising, and segmentation.

1. Dataset Overview

• Set5, Set11, Set14: Small-scale datasets for super-resolution, containing diverse image categories for testing reconstruction performance.
• BSD100, BSD500: Natural scene images for denoising, segmentation, or feature extraction studies, with varying resolutions and complexities.
• McM18: Medical image dataset for tasks like reconstruction and enhancement. Suitable for domain-specific testing in healthcare applications.
• Urban100: Urban scene dataset, ideal for super-resolution models focusing on high-frequency textures and structural details.

2. General Instructions

1. Download: Ensure datasets are downloaded from their official sources to maintain integrity.
2. Preprocessing: Use standard preprocessing techniques (e.g., resizing, normalization) as per the specific research requirement.
3. Partitioning: Split datasets into training, validation, and testing sets if not predefined.
4. Usage: Apply appropriate data augmentations (e.g., flipping, rotation) to enhance model robustness.

3. Evaluation Metrics

• Common metrics include PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) for assessing reconstruction and image quality.
• For segmentation, use IoU (Intersection over Union) or Dice Coefficient.

4. Best Practices

• Maintain consistent preprocessing pipelines across datasets for comparable results.
• Document dataset configurations and processing steps in publications or experiments for reproducibility.
• Cite the datasets appropriately in academic work.

By following these instructions, you can maximize the utility of these datasets in your research and ensure rigorous evaluation.