用双近似器布朗桥模型实现高保真无噪图像翻译
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators
- 基于布朗桥动力学与双向神经近似器生成图像
- 在超分辨率任务中输出误差低于0.01，接近真实图像
- 适合需要稳定、可复现结果的图像生成场景
图像到图像（I2I）翻译旨在将图像从一个域转换到另一个域。确定性I2I翻译（如图像超分辨率）要求每个输入产生一致且可预测的输出，与真实图像（GT）高度匹配。本文提出一种基于双近似器的去噪布朗桥模型（Dual-approx Bridge），利用布朗桥动态特性，结合前向与反向过程的两个神经网络近似器，生成方差极小、质量高的输出。在包括图像生成和超分辨率在内的多个基准数据集上，该模型在图像质量和对真实标签的忠实度方面均显著优于随机与确定性基线方法。
原文摘要 · Abstract (English)
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: https://github.com/bohan95/dual-app-bridge
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