Convolutional Edge Diffusion for Fast Contrast-guided Image Interpolation

A recently introduced image interpolation method, called the contrast-guided interpolation (CGI), has shown superior performance on producing high-quality interpolated image. However, its iterative edge diffusion (IED) process for diffusing continuous-valued directional variation (DV) fields inevitably incurs high computational complexity due to its iterative optimization process. The key objective of this letter lies in how to greatly reduce the computation of this diffusion process while maintaining CGI's superior performance on its interpolated image. The novelty of this letter started with a critical observation as follows. Since each diffused DV field needs to be thresholded for generating a binary contrast-guided decision map (CDM) in the subsequent step, such binarization operation will definitely destroy the fidelity that was preserved previously through the data term of the IED's energy functional. Therefore, the data term is lifted in our approach to yield a new energy functional. It turns out that the diffusion equation derived from this simplified functional is, in fact, the well-known heat equation, from which a highly attractive property of the heat equation can be exploited for conducting diffusion. That is, given a desired amount of diffusion to yield, it can be realized by simply convolving the DV field with a Gaussian kernel once, rather than gradually updating the DV field through iterations. Note that the variance of the Gaussian kernel corresponds to the amount of diffusion desired. As a result, the total computation time is significantly reduced. Extensive simulation results have shown that the proposed CED can generate nearly identical CDMs as those produced by the IED, while only requiring about 1/10 of its computation time. By replacing the IED with the proposed CED in the CGI framework, the total run time of our fast CGI is only 1/4 of the original CGI's on average.