Bayesian nights: Optimizing night photography rendering with Bayesian derivative-free methods

作     者：Zini, Simone Buzzelli, Marco 

作者机构：Univ Milano Bicocca Dept Informat Syst & Commun Viale Sarca 336 I-20126 Milan Italy 

出 版 物：《PATTERN RECOGNITION》 (Pattern Recogn.)

年 卷 期：2025年第161卷

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Bayesian optimization Low-light image enhancement Image signal processing Camera pipeline Machine learning 

摘      要：We introduce a novel approach for optimizing Image Signal Processing (ISP) rendering pipelines for night photography through a Bayesian derivative-free procedure. Traditional neural-network-based ISPs depend on differentiable operations to enable backpropagation-based optimization, a requirement that can impose significant constraints. Our method circumvents this by employing Bayesian optimization to fine-tune the pipeline s parameters, independently of their differentiability. Additionally, we address the need for paired data to enable supervised optimization: while such paired data is available on public datasets, it is expensive to collect for new imaging devices. To this extent, we design a raw-to-raw mapping procedure, that aligns images from an available paired dataset to the target unpaired dataset. This allows us to supervise the optimization of our solution directly within the target space, without the need for device-specific paired data. We validate our approach with extensive experimentation on paired and unpaired datasets, demonstrating its efficacy using both subjective and objective evaluation metrics. Our code is made available for public download at https://***/TheZino/Bayesian-pipeline-optimization.