  Diffusion models have proven effective for various applications such as
images, audio and graph generation. Other important applications are image
super-resolution and the solution of inverse problems. More recently, some
works have used stochastic differential equations (SDEs) to generalize
diffusion models to continuous time. In this work, we introduce SDEs to
generate super-resolution face images. To the best of our knowledge, this is
the first time SDEs have been used for such an application. The proposed method
provides an improved peak signal-to-noise ratio (PSNR), structural similarity
index measure (SSIM), and consistency than the existing super-resolution
methods based on diffusion models. In particular, we also assess the potential
application of this method for the face recognition task. A generic facial
feature extractor is used to compare the super-resolution images with the
ground truth and superior results were obtained compared with other methods.
Our code is publicly available at https://github.com/marcelowds/sr-sde
