MATLAB Simulation Programs for Image Denoising

The text describes three MATLAB simulation programs addressing key image processing tasks: image denoising, point spread function (PSF) acquisition, and constrained least squares image restoration. These programs implement sophisticated algorithms that require understanding of both theoretical foundations and practical implementation details. The image denoising program typically employs algorithms such as Wiener filtering, median filtering, or wavelet-based denoising methods. The implementation involves noise type detection (Gaussian, salt-and-pepper, etc.) and adaptive parameter selection based on noise level estimation. Key functions may include imnoise() for noise addition and customized filtering functions with threshold optimization. The PSF acquisition program utilizes frequency domain analysis through Fourier transforms (fft2/ifft2) and deconvolution techniques. It may implement blind deconvolution algorithms or use known pattern analysis to estimate the blur kernel. The program likely includes point source image analysis and modulation transfer function calculations. The constrained least squares restoration program implements regularization methods with Tikhonov regularization or total variation constraints. The code would solve optimization problems using matrix operations (often via pinv() or lsqlin() functions) while maintaining boundary conditions and noise constraints. The implementation balances computational efficiency with restoration quality through iterative refinement. Understanding these underlying principles is crucial for effective application. The denoising algorithm's performance depends on accurate noise characterization, while PSF retrieval requires appropriate image sampling and calibration. The constrained least squares method involves trade-offs between restoration accuracy and computational resources, often controlled through regularization parameter selection. Therefore, successful implementation requires not only running these MATLAB programs but also comprehending the mathematical models, algorithm selection criteria, and parameter optimization strategies to adapt them to various imaging scenarios and achieve optimal processing results.