Converting Binary Images to Grayscale Using Neighborhood Average Two-Dimensional Histograms
Transforming binary images into grayscale representations by employing neighborhood average 2D histograms to preserve enhanced image detail and information
Image Denoising Techniques and Implementation
Image Denoising: Implement various noise types (random pixel noise, salt-and-pepper noise) and compare denoising methods including neighborhood averaging, median filtering, and image stacking with algorithmic analysis and code implementation approaches.
Image Grayscale Transformation and Image Sharpening
Implementation of image processing techniques including: (1) histogram equalization, (2) histogram matching, (3) neighborhood averaging, (4) local enhancement, and (5) median filtering. Complete source code provided for these fundamental image processing operations.
Neighborhood Averaging in Image Processing
Neighborhood Averaging for Image Smoothing