function g = bfilt_gray(f,r,a,b) % f灰度图；r滤波半径；a全局方差；b局部方差 [x,y] = meshgrid(-r:r); w1 = exp(-(x.^2+y.^2)/(2*a^2)); f = tofloat(f);%f = im2double(f); h = waitbar(0,'Applying bilateral filter...'); set(h,'Name','Bilateral Filter Progress'); [m,n] = size(f); f_temp = padarray(f,[r r],'symmetric'); g = zeros(m,n); for i = r+1:m+r for j = r+1:n+r temp = f_temp(i-r:i+r,j-r:j+r); w2 = exp(-(temp-f(i-r,j-r)).^2/(2*b^2)); w = w1.*w2; s = temp.*w; g(i-r,j-r) = sum(s(:))/sum(w(:)); end waitbar((i-r)/m); end

This appears to be a MATLAB function definition for a bilateral filter applied to a grayscale image. The function takes in four arguments: the grayscale image (f), the filter radius (r), the global variance (a), and the local variance (b).

The function first creates a meshgrid for the filter radius and calculates the weights for the global variance. It then converts the input grayscale image to floating point values, creates a progress bar, and pads the image with the filter radius using symmetric padding.

A loop is then run through the image, where for each pixel, a sub-matrix is created using the filter radius, and the weights for the local variance are calculated. The final weight for each pixel is the product of the global and local weights.

The pixel values are then multiplied by the weights, summed and divided by the sum of the weights to get the filtered output. The progress bar is updated with the loop index at each iteration. Finally, the filtered output is returned as the output of the function.