ABDGAN: Arbitrary Time Blur Decomposition Using Critic-Guided TripleGAN.

ABSTRACT: Recent studies have proposed methods for extracting latent sharp frames from a single blurred image. However, these methods still suffer from limitations in restoring satisfactory images. In addition, most existing methods are limited to decomposing a blurred image into sharp frames with a fixed frame rate. To address these problems, we present an Arbitrary Time Blur Decomposition Triple Generative Adversarial Network (ABDGAN) that restores sharp frames with flexible frame rates. Our framework plays a min-max game consisting of a generator, a discriminator, and a time-code predictor. The generator serves as a time-conditional deblurring network, while the discriminator and the label predictor provide feedback to the generator on producing realistic and sharp image depending on given

SUBMITTER: Lee TB 

PROVIDER: S-EPMC11314794 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature