An Efficient Deep Neural Network Based Image Enhancement with Improved Filtering Technics

Paper explores the challenging problem of text-to-image synthesis within the domain of generative modeling. The task involves generating coherent and realistic images from textual descriptions, a critical aspect of automatic learning for applications such as image creation, modification, analysis, and optimization. In response to existing limitations in scene understanding, especially in synthesizing coherent structures in complex scenes, we introduce a novel model called CapGAN. CapGAN utilizes skip-thought vectors to encode given text statements into vector representations, serving as inputs for image synthesis through an adversarial process involving a generator (G) and discriminator (D). The distinguishing feature of CapGAN lies in the integration of capsules at the discriminator level, enriching the model's comprehension of orientational and relative spatial relationships within different entities of an object in an image.