Related Work
ISP tuning.

Recent developments in end-to-end AI-based ISP pipelines show potential as alternatives to traditional mobile ISPs ignatov2020replacing ; jeong2022rawtobit ; shekhar2022transform . Yet, traditional parametric ISPs remain preferred in consumer cameras for their controllability, efficiency, and interpretability. These systems require expert-driven, labor-intensive tuning to enhance image quality and achieve desired aesthetic effects. Efforts to streamline this process are ongoing, aiming to reduce the need for manual adjustments.
Gradient-based optimization is a notable strategy in this area. Tseng et al. tseng2019hyperparameter have applied differentiable black-box proxies to simplify ISP tuning. Further research by Tseng et al. tseng2022neural has opened up the ISP pipeline into white-box manageable modules, learning differentiable proxies for each module and subsequently integrating them, which improves the tuning performance.
Additionally, Qin et al. qin2022attention introduced an attention-based CNN approach for scene-aware ISP tuning. However, this method lacks integration of sequence-specific prior knowledge. They later developed a framework for predicting ISP hyper-parameters sequentially qin2023learning , optimizing parameters based on their relationships and similarities.
In a different approach, Mosleh et al. mosleh2020hardware utilized a genetic evolutionary algorithm with a zero-order stochastic solver hansen2006cma to directly optimize hardware-specific image processing pipelines, circumventing the constraints of gradient-based methods.
Moreover, Nishimura et al. nishimura2018automatic explored derivative-free optimization, employing nonlinear techniques and automatic reference generation for effective automation of image quality adjustments.
Despite these advancements, the complexity of the image processing pipeline and the vast parameter space continue to challenge the efficiency of tuning methods.

Photo stylization. Automating image stylization, which is straightforward for humans, poses challenges for machines. Significant research has been conducted to bridge this gap. Karras et al. karras2019style pioneered StyleGAN, a network manipulating the latent space to control image styles at various scales. Building on this, Brooks et al. brooks2023instructpix2pix developed InstructPix2Pix, which allows users to guide the stylization process through textual instructions, enhancing user-machine interaction. However, these methods often lack explainability and may alter the original image content.
Further exploring transparency, Hu et al. hu2018exposure proposed a reinforcement learning-based white-box photo-finisher, though its need for explicit gradients limits compatibility with traditional systems. Kosugi et al. kosugi2020unpaired addressed style diversity using unpaired data based on reinforcement learning. Despite these advances, these methods are restricted to a single style during training, limiting the user’s ability to control the pipeline to produce images of any desired style during inference.
Moreover, Tseng et al. tseng2022neural optimized proxy networks for image processing modules via a style loss function, achieving promising results but facing limitations in handling complex modules and style variability during inference.