Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators

TL;DR

This paper introduces a novel deterministic image-to-image translation model using denoising Brownian bridge dynamics with dual neural network approximators, achieving high fidelity and consistency in outputs.

Contribution

It proposes a new generative model that leverages Brownian bridge processes and dual approximators for deterministic I2I translation, improving output faithfulness and quality.

Findings

Outperforms stochastic and deterministic baselines in image quality

Produces outputs with negligible variance and high fidelity

Demonstrates effectiveness on benchmark datasets for super-resolution

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: https://github.com/bohan95/dual-app-bridge