A Non-Linear Method to Interpolate Binary Images Using Location and
Neighborhood Adaptive Rules

This is a paper on some interesting research on image interpolation. This was part of my PhD work. Results have been better than hoped for albeit with certain major limitations and I intend to take this forward.


In this paper, we propose a new zooming technique
for binary images, using location and neighborhood adaptive,
non-linear interpolation rules. These rules are inspired by the
way an artist would draw an enlarged image. Using simple
examples, we show that the output generated by popular
interpolation techniques is very different from what a human
does. Our rules are based on such observations for simple
objects, and they try to mimic what an artist does. Using these
rules, we interpolate complex images and demonstrate the
impact. We compare our method with bicubic interpolation
and show that our method gives better visual quality. We also
show that, on an average, our method results in higher PSNR
and a lower MPSNR. The SSIM of the output images are
nearly the same for both methods. Our method overcomes a
number of problems associated with known interpolation
techniques, such as blurring and thickening of edges. Our
method uses a set of sixteen rules in five categories. Each pixel
in the interpolated image is computed by a chosen rule. The
choice depends on the location of the pixel and the content in
the neighborhood. The size of the neighborhood varies. Some
rules can be influenced by distant pixels in the input and can
impact distant pixels in the output. We present examples
showing the effectiveness of our method. The results are
visually appealing. We show that lines and dots, with single
pixel thickness, retain their thickness. Inclined lines and solids
don’t develop as much jaggedness as happens with bicubic
interpolation. Similarly, curves are also relatively smoother.

Here is the whole paper: Link to online journal where published

A few sample results:

Location and Neighborhood Adaptive Interpolation

Here are a few other links you may like:

A Yahoo Videos story

In which Annie gives it those ones