Salt and Pepper Noise Dataset:Clean vs Noisy Image

The Salt and Pepper Noise Dataset is an essential resource for researchers, developers, and machine learning enthusiasts focused on image processing and computer vision. This dataset is designed to facilitate the study of noise effects and the development of advanced denoising algorithms.

Key Features:

1. Dual Image Sets: The dataset includes two clearly defined categories:

   • Pristine Images: A collection of high-quality, noise-free images for baseline analysis.
   • Noisy Images: Images intentionally infused with salt and pepper noise to simulate real-world noise scenarios.

2. Wide Application Scope:

   • Ideal for evaluating image processing algorithms.
   • Suitable for testing and improving noise reduction techniques.
   • Beneficial for training machine learning models to handle noisy data.

3. Research-Oriented Design:

   • Provides a controlled environment for studying the impact of salt and pepper noise.
   • Supports experiments in image restoration, feature extraction, and algorithm benchmarking.

This dataset is sourced from Kaggle.