Fusion-Based Backlit Image Enhancement Using Multiple S-Type Transformations For Convex Combination Coefficients

This research study proposes a compatible encoder-enabled video generating method. The encoder-enabled method adds an inference mechanism for enhancing the ability of Generative Adversarial Networks (GAN) based video generators. The proposed video generating method is called Encoding GAN3 (EncGAN3) and decomposes the video into two streams representing content and movement, respectively. The proposed model consists of three processing modules, representing Encoder, Generator and Discriminator, each trained separately, by considering its own loss function. EncGAN3 is shown to generate videos of high quality, according to both visual and numerical results.