Image Weighted Averaging Techniques for Digital Image Processing

Image weighted averaging is an image processing technique that generates a new image by computing weighted averages for each pixel. This method assigns distinct weights to different pixels, enabling enhanced representation of original image characteristics in the output. Key applications include image enhancement, image fusion, and image denoising. Through iterative weighted averaging cycles (e.g., implementing multiple passes with numpy or OpenCV's addWeighted() function), gradual modifications to image appearance and quality can be achieved for specific objectives. The algorithm typically involves: 1. Defining weight matrices (e.g., Gaussian kernels for smooth blending) 2. Applying element-wise multiplication between source pixels and weights 3. Summing weighted values across contributing images This flexible technique supports both linear combinations (using cv2.addWeighted(src1, alpha, src2, beta, gamma)) and custom nonlinear weighting schemes, making it a powerful tool for computational photography and computer vision workflows.