You could get fringes from a variety of ways. It could be a Moire pattern (aliasing), it could be noise (hum or voltage line frequency noise), it could be Gibbs noise due to filtering, it could be some artifact of your reconstruction of image processing methods, etc. If the fringes are periodic they would show up as spikes in your Fourier Transform. You could then zero them out and inverse transform. You could try a bandstop filter with median filters, like this demo:
% Program to remove annoying ripples in an image by a band stop filter.
% by ImageAnalyst
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
imtool close all; % Close all imtool figures.
clear; % Erase all existing variables.
workspace; % Make sure the workspace panel is showing.
fontSize = 20;
% Change the current folder to the folder of this m-file.
% (The line of code below is from Brett Shoelson of The Mathworks.)
if(~isdeployed)
cd(fileparts(which(mfilename)));
end
originalGrayImage = im2double(imread('whitehouse.tif'));
subplot(2,2,1);
imshow(originalGrayImage);
title('Original Grayscale Image', 'FontSize', fontSize);
set(gcf, 'Position', get(0,'Screensize')); % Enlarge figure to full screen.
lowPassWindowWidth = 21;
highPassWindowWidth = 7;
Idm = medfilt2(originalGrayImage, [lowPassWindowWidth lowPassWindowWidth]);
subplot(2,2,2);
imshow(Idm);
title('Lowpass Image', 'FontSize', fontSize);
Ide = originalGrayImage - medfilt2(originalGrayImage, [highPassWindowWidth highPassWindowWidth]);
subplot(2,2,3);
imshow(Ide);
title('Highpass Image', 'FontSize', fontSize);
finalFilteredGrayImage = Idm + Ide;
subplot(2,2,4);
imshow(finalFilteredGrayImage);
caption = sprintf('Final Filtered Image =\nLow Pass Image + High Pass Image');
title(caption, 'FontSize', fontSize);
