"Electrical Impedance Tomography Images for breast cancer detection - Distorted using MATLAB's Image Processing Toolbox"

"This dataset was developed to support research in Electrical Impedance Tomography (EIT) image enhancement using super-resolution (SR) techniques. It comprises simulated conductivity distributions generated from a multilayer breast phantom model implemented in COMSOL Multiphysics 6.2, representing both healthy and tumour-present scenarios. To reflect realistic imaging conditions, the dataset includes systematically degraded images produced through the application of Gaussian noise at varying signal-to-noise ratio (SNR) levels, motion blur, and geometric warp transformations. Both individual and combined distortions are provided to simulate practical challenges such as patient movement and measurement noise. Corresponding high-resolution (HR) ground-truth images are also included, enabling supervised learning and quantitative evaluation.The dataset is structured to support benchmarking of super-resolution algorithms. Evaluation of model performance can be conducted using standard image quality metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Contrast-to-Noise Ratio (CNR).This dataset is intended for use in both biomedical imaging research, particularly for breast cancer detection, and broader applications of EIT in industrial inspection and process monitoring. "