add 90% salt and pepper noise and remove it with one of the latest methods in the literature (imf). interpret the filtered image. compare the filter you used with the median filter. in matlab

To add 90% salt and pepper noise to an image, you can use the imnoise() function in MATLAB. The function can add different types of noise. For salt and pepper noise, you need to specify the 'salt & pepper' option. Here's an example code to add 90% salt and pepper noise to an image:

main.m
img = imread('image.jpg'); % read the image
noisy_img = imnoise(img, 'salt & pepper', 0.9); % add 90% salt and pepper noise
124 chars
3 lines

To remove the noise using the Intrinsic Mode Function (IMF) method, we can use the EMD (Empirical Mode Decomposition) technique. Here's an example code to apply EMD on the noisy image:

main.m
imf = emd2(double(noisy_img), 'Display', 0); % apply EMD
denoised_img = imf(:, :, end); % the last IMF contains the signal
123 chars
3 lines

The filtered image can be interpreted as a denoised image with less distortion caused by the noise. The EMD method decomposes the image into different IMFs (Intrinsic Mode Functions) that represent different frequency bands. The last IMF contains the signal without the noise.

To compare the EMD method with the median filter, we can apply the median filter to the noisy image and compare the results. Here's an example code to apply the median filter:

main.m
median_filtered = medfilt2(noisy_img); % apply the median filter
65 chars
2 lines

We can compare the results by calculating the PSNR (Peak Signal-to-Noise Ratio) between the original image and the filtered image. Higher PSNR value means better image quality. Here's an example code to calculate the PSNR:

main.m
psnr_noisy = psnr(noisy_img, img); % PSNR between noisy and original image
psnr_median = psnr(median_filtered, img); % PSNR between median filtered and original image
psnr_emd = psnr(denoised_img, img); % PSNR between EMD filtered and original image
250 chars
4 lines

We can also visualize the results to see how each method performs visually. Here's an example code to visualize the results:

main.m
subplot(2,2,1), imshow(img); title('Original Image');
subplot(2,2,2), imshow(noisy_img); title('Noisy Image');
subplot(2,2,3), imshow(median_filtered); title('Median Filtered Image');
subplot(2,2,4), imshow(denoised_img); title('EMD Filtered Image');
251 chars
5 lines

In general, the EMD method can preserve finer details of the image better than the median filter. However, it might take more time to process than the median filter.