Part II - Edge-Based Sharpening

Explanation

Edge-based Sharpening applies Laplacian filtering, hence using the convolution method from Part I - Edge Detector in the first assignment. Instead of applying it on one image, the filter is applied onto every frame.

As for calculating edge based on frame difference, the previous frame data is stored when processing the next frame. By having data for both frames, the differences are obtained by subtracting the pixel data of the current frame from the previous frame. This difference is then placed onto the front canvas to be displayed.

Below is a sample screenshot:

Reflection

Right, the convolution process again. The algorithm has already mind-boggled me in Part I, and it came back to haunt me here again. But rather than shying away, I think I learnt new things here too. Laplacian filter, as we all know, is only a matrix of values. Change these values and you might get another effect including:-

• sharpened images
• blurred images
• Sobel filtering (horizontal/vertical)

So since we can do all these using a single algorithm with different matrix values, I wonder...would I get the same effect if I used other different algorithms? If the effects are different, how do we determine which one is best? How do we determine which is the one we really want? And how would I know which I should use? More and more interesting questions, but let's keep that for another time.