High-Resolution Image Translation Model Based on Grayscale Redefinition
This work addresses image translation for computer vision applications, but it appears incremental as it builds on existing methods like Pix2PixHD with modifications.
The paper tackles high-resolution image translation by using grayscale adjustment for pixel-level translation and adapting Pix2PixHD with enhancements like a coarse-to-fine generator and multi-scale discriminator, while addressing sparse data through model weight initialization from other tasks, achieving improved performance in image translation tasks.
Image-to-image translation is a technique that focuses on transferring images from one domain to another while maintaining the essential content representations. In recent years, image-to-image translation has gained significant attention and achieved remarkable advancements due to its diverse applications in computer vision and image processing tasks. In this work, we propose an innovative method for image translation between different domains. For high-resolution image translation tasks, we use a grayscale adjustment method to achieve pixel-level translation. For other tasks, we utilize the Pix2PixHD model with a coarse-to-fine generator, multi-scale discriminator, and improved loss to enhance the image translation performance. On the other hand, to tackle the issue of sparse training data, we adopt model weight initialization from other task to optimize the performance of the current task.