MATLAB Code Implementation for Ghost Imaging (GI)

Ghost Imaging (GI) is an indirect imaging technique based on light field intensity correlation calculations. Its core principle involves reconstructing object images through correlation algorithms by measuring the interaction between speckle patterns and target objects. Implementing ghost imaging in MATLAB typically requires solutions for the following key components:

### 1. Data Reading and Preprocessing GI processing requires two types of data: sequences of speckle patterns from reference light fields (typically from pre-generated random matrices or experimentally acquired data) and intensity signals from bucket detectors. In MATLAB, use `imread` or custom functions to read speckle images, storing bucket signals as 1D arrays. For large datasets, consider batch reading or memory mapping techniques (e.g., `memmapfile`) for performance optimization.

### 2. Correlation Algorithm Implementation Classical ghost imaging reconstructs images through second-order intensity correlation using the formula: [ G(x,y) = langle I_b cdot I_r(x,y) rangle - langle I_b rangle langle I_r(x,y) rangle ] where (I_b) represents bucket detector signals and (I_r(x,y)) denotes reference light field intensity. MATLAB enables efficient implementation through matrix operations: - Reshape speckle sequences into 2D matrices (each row represents vectorized data from one speckle pattern) - Compute covariance between bucket signals and speckle matrices using `cov` function or direct vectorized operations - Apply normalization and image reconstruction, potentially incorporating denoising algorithms like wavelet transforms

### 3. Storage and Optimization Data Storage: Save reconstructed images in `uint16` format (using `imwrite`) to preserve dynamic range, while intermediate variables can be stored as `.mat` files (`save` function) for subsequent analysis. Computational Acceleration: Utilize parallel computing (`parfor`) or GPU processing (`gpuArray`) for large-scale correlation operations to significantly improve efficiency.

### 4. Advanced Implementation Approaches Compressive Sensing GI: Integrate sparse reconstruction algorithms (e.g., Orthogonal Matching Pursuit - OMP) to reduce required measurement counts. Deep Learning Integration: Employ CNN networks to directly predict images from bucket signals, suitable for real-time applications.

Note: Practical implementation requires parameter adjustments based on experimental configurations (speckle size, sampling rate) and algorithm validation for noise robustness.