Inverse Halftoning is tough!!! Yeah, thats my final conclusion. No I have not given up..I am just frustrated that things are not working properly. I pretty much explained the steps involved in a halftoning process using wavelets in my last post. I realized later that I should not pass the ‘S’ component through a gaussian filter as this makes the final image more blurrier. After all the whole point of the process is to recover edges from the halftoned image.
After recovering them, the more difficult part is to blend it with the image. One paper suggested passing horizontal and vertical edges (from the DWT) through a low pass filter. This will reduce the noise and highlight the main edges. Well it does highlight the main edges but also highlights a lot of extra stuff. Supposedly (as suggested in another paper), this does not happen in error-diffused halftoned images. Moreover, the method of low pass filtering only works well when the halftoned images is error-diffused. A real bummer becuase the authors of the paper claimed the algorithm worked for any kind of halftoned image and that is what got me to implement the algorithm. Anyways, I have got a pretty good grasp of what is going on and should be able to come up with a general algorithm.
Its getting late now. My plan for tomorrow is to test the algorithm on a newspaper image that I have scanned. That should be interested because its a more practical situation.
