From Cheap to Pro: A Learning-based Adaptive Camera Parameter Network for Professional-Style Imaging

TL;DR

This paper introduces ACamera-Net, a lightweight, adaptive neural network that predicts optimal camera parameters from RAW images to improve image quality under complex lighting conditions, suitable for real-time edge device deployment.

Contribution

The paper presents a novel scene-adaptive camera parameter network that directly predicts exposure and white balance settings, improving image quality without additional enhancement modules.

Findings

Outperforms conventional auto modes in diverse lighting conditions

Achieves real-time inference on edge devices

Enhances image stability and quality across scenarios

Abstract

Consumer-grade camera systems often struggle to maintain stable image quality under complex illumination conditions such as low light, high dynamic range, and backlighting, as well as spatial color temperature variation. These issues lead to underexposure, color casts, and tonal inconsistency, which degrade the performance of downstream vision tasks. To address this, we propose ACamera-Net, a lightweight and scene-adaptive camera parameter adjustment network that directly predicts optimal exposure and white balance from RAW inputs. The framework consists of two modules: ACamera-Exposure, which estimates ISO to alleviate underexposure and contrast loss, and ACamera-Color, which predicts correlated color temperature and gain factors for improved color consistency. Optimized for real-time inference on edge devices, ACamera-Net can be seamlessly integrated into imaging pipelines. Trained on diverse real-world data with annotated references, the model generalizes well across lighting conditions. Extensive experiments demonstrate that ACamera-Net consistently enhances image quality and stabilizes perception outputs, outperforming conventional auto modes and lightweight baselines without relying on additional image enhancement modules.