In this code, a linear equation is used to generate sample data using a slope and bias. Later a Gaussian noise is added to the desired output. The noisy output and original input is used to determine the slope and bias of the linear equation using LMS algorithm. This implementation of LMS is based on batch update rule of gradient decent algorithm in which we use the sum of error instead of sample error. You can modify this code to create sample based update rule easily.
Shujaat Khan (2020). Least Mean Square (LMS) (https://www.mathworks.com/matlabcentral/fileexchange/60080-least-mean-square-lms), MATLAB Central File Exchange. Retrieved .
@heena panda (1) you will require two dimensional filter to process image data. (2) for specific problem you need to redefine your cost function.
How can I apply it on image deblurring after determination of the length of blur and angle of the blur?
well commented and easy to understand, good job
Inspired: Constrain Least Mean Square Algorithm