Image-to-Image Translation with Conditional Adversarial Networks

作者：Marjana Tahmid, Md Samiul Alam, Namratha Rao, Kazi Muhammad Asif Ashrafi · 年份：2023 · DOI：10.1109/wiecon-ece60392.2023.10456447 · 被引用次数：65 · 研究领域：Image Processing Techniques and Applications、Generative Adversarial Networks and Image Synthesis、Advanced Image Processing Techniques

Several syntheses of photographs based on label maps, restoration of objects using edge maps, and image colorizing, and many others traditionally requires designing individual/unique loss function for each task. These tasks have one thing in common: they can be treated as a single Image to the Image translation problem. Convolution neural networks (CNNs) have become popular for a wide variety of image-related problems. The Image-to-Image translation problem can be solved with one general approach, which is using the conditional adversarial network. We have implemented conditional adversarial networks. The specialty of this network is they acquire insight on a loss function to train this mapping in addition to learning the mapping to output image from input image.