Image Filtering and Noise Estimation
a project with a background image
Developed a noise estimation algorithm on additive Gaussian noise images with a standard deviation of 2.0 and accurately estimated the noise level with a standard deviation of 1.92 – 2.03. Filtered the images by implementing Median, Box, and Gaussian filters with an accuracy of 97% compared to conventional filters. Performed Prewitt and Sobel Masks for edge detection and further used eigenvalue decomposition for corner detection.