Image Noise

In research papers, we can consider adding different types of noise to images, such as Gaussian noise, salt-and-pepper noise, or Poisson noise. This approach enables more comprehensive evaluation of image processing algorithms and provides additional experimental results to support our research. From an implementation perspective, Gaussian noise can be added using the MATLAB function 'imnoise(I,'gaussian',mean,variance)' where mean and variance parameters control noise intensity. Salt-and-pepper noise implementation typically involves randomly selecting pixel locations and setting them to maximum or minimum values using 'imnoise(I,'salt & pepper',density)'. Poisson noise simulation often requires generating random values from Poisson distributions corresponding to pixel intensities. Furthermore, we can explore image restoration methods under different noise levels to compare their effectiveness and robustness. This can be implemented by creating noise-level parameters that scale the noise intensity, allowing systematic testing of denoising algorithms like median filtering, Wiener filtering, or wavelet-based methods. Through such expansion, we can better understand the challenges and solutions in the image processing field, providing more references and insights for future research. The code implementation typically involves creating standardized test benches with configurable noise parameters and quantitative evaluation metrics like PSNR and SSIM for objective performance comparison.