Abstract
Image-to-Image (I2I) translation involves converting an image from one domain to another. Deterministic I2I translation, such as in image super-resolution, extends this concept by guaranteeing that each input generates a consistent and predictable output, closely matching the ground truth (GT) with high fidelity. In this paper, we propose a denoising Brownian bridge model with dual approximators (Dual-approx Bridge), a novel generative model that exploits the Brownian bridge dynamics and two neural network-based approximators (one for forward and one for reverse process) to produce faithful output with negligible variance and high image quality in I2I translations. Our extensive experiments on benchmark datasets including image generation and super-resolution demonstrate the consistent and superior performance of Dual-approx Bridge in terms of image quality and faithfulness to GT when compared to both stochastic and deterministic baselines. Project page and code: this https URL
Abstract (translated)
图像到图像（Image-to-Image, I2I）转换涉及将一幅图像从一个领域转换为另一个领域。确定性的I2I转换，如超分辨率图像处理，则进一步确保每个输入都会生成一致且可预测的输出，并与高保真的真实地面（Ground Truth, GT）紧密匹配。在本文中，我们提出了一种新的生成模型——去噪布朗桥模型结合双近似器（Dual-approx Bridge），该模型利用了布朗桥动力学和两个基于神经网络的近似器（一个用于前向过程，另一个用于逆向过程），以产生具有几乎无方差且高质量图像输出的I2I转换。我们通过基准数据集上的广泛实验，包括图像生成和超分辨率任务，展示了Dual-approx Bridge在图像质量和接近真实地面（GT）方面的持续卓越性能，与随机和确定性的基线方法相比。 项目页面和代码：[此链接](https://this-url.com/)
URL
https://arxiv.org/abs/2512.23463
