Calculating Normalized Mean Square Error Between Recovered and Original Images Through Programming
- Login to Download
- 1 Credits
Resource Overview
Through computational programming, the normalized mean square error between the restored image and the original image is determined as:
Detailed Documentation
In this context, we can programmatically compute the normalized mean square error (NMSE) between the restored image and the original image. This error metric helps quantify the dissimilarity between the recovered and reference images. By calculating NMSE, we gain valuable insights into the quality of image restoration and can objectively evaluate the performance of reconstruction algorithms. The implementation typically involves pixel-wise difference calculation, squared error summation, and normalization by the original image's energy, often executed using matrix operations in programming environments like Python (with NumPy) or MATLAB.
- Login to Download
- 1 Credits