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

• URL: http://arxiv.org/abs/2510.20550v1
• Date: Thu, 23 Oct 2025 13:35:17 GMT
• Title: From Cheap to Pro: A Learning-based Adaptive Camera Parameter Network for Professional-Style Imaging
• Authors: Fuchen Li, Yansong Du, Wenbo Cheng, Xiaoxia Zhou, Sen Yin,
• Abstract summary: ACamera-Net is a lightweight and scene-adaptive camera parameter adjustment network.<n>It predicts optimal exposure and white balance from RAW inputs.<n>It consistently enhances image quality and stabilizes perception outputs.
• Score: 0.07829352305480283
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Unifying Color and Lightness Correction with View-Adaptive Curve Adjustment for Robust 3D Novel View Synthesis [73.27997579020233]
  We propose Luminance-GS++, a 3DGS-based framework for robust NVS under diverse illumination conditions.<n>Our method combines a globally view-adaptive lightness adjustment with a local pixel-wise residual refinement for precise color correction.
  arXiv  Detail & Related papers  (2026-02-20T16:20:50Z)
• LightQANet: Quantized and Adaptive Feature Learning for Low-Light Image Enhancement [65.06462316546806]
  Low-light image enhancement aims to improve illumination while preserving high-quality color and texture.<n>Existing methods often fail to extract reliable feature representations due to severely degraded pixel-level information under low-light conditions.<n>We propose LightQANet, a novel framework that introduces quantized and adaptive feature learning for low-light enhancement.
  arXiv  Detail & Related papers  (2025-10-16T14:54:42Z)
• Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark [58.61079960074608]
  Existing infrared image enhancement methods focus on tackling individual degradations.<n>All-in-one enhancement methods, commonly applied to RGB sensors, often demonstrate limited effectiveness.
  arXiv  Detail & Related papers  (2025-10-10T12:55:54Z)
• LiDAR Point Cloud Colourisation Using Multi-Camera Fusion and Low-Light Image Enhancement [0.0]
  This study introduces a novel, hardware-agnostic methodology that generates colourised point clouds from mechanical LiDAR using multiple camera inputs.<n>The primary innovation lies in its robustness under low-light conditions, achieved through the integration of a low-light image enhancement module.<n>The algorithm was tested using a Velodyne Puck Hi-Res LiDAR and a four-camera configuration.
  arXiv  Detail & Related papers  (2025-09-30T06:56:11Z)
• Time-Aware Auto White Balance in Mobile Photography [32.74472735591053]
  We introduce a dataset of 3,224 smartphone images with contextual metadata collected at various times of day and under diverse lighting conditions.<n>The dataset includes ground-truth illuminant colors, determined using a color chart, and user-preferred illuminants validated through a user study.
  arXiv  Detail & Related papers  (2025-04-08T02:45:37Z)
• Luminance-GS: Adapting 3D Gaussian Splatting to Challenging Lighting Conditions with View-Adaptive Curve Adjustment [46.60106452798745]
  We introduce Luminance-GS, a novel approach to achieving high-quality novel view synthesis results under challenging lighting conditions using 3DGS.<n>By adopting per-view color matrix mapping and view-adaptive curve adjustments, Luminance-GS achieves state-of-the-art (SOTA) results across various lighting conditions.<n>Compared to previous NeRF- and 3DGS-based baselines, Luminance-GS provides real-time rendering speed with improved reconstruction quality.
  arXiv  Detail & Related papers  (2025-04-02T08:54:57Z)
• SEE: See Everything Every Time - Adaptive Brightness Adjustment for Broad Light Range Images via Events [62.23917631871362]
  Event cameras, with a high dynamic range exceeding $120dB$, significantly outperform traditional embedded cameras.<n>We propose a novel research question: how to employ events to enhance and adaptively adjust the brightness of images captured under broad lighting conditions.<n>Our framework captures color through sensor patterns, uses cross-attention to model events as a brightness dictionary, and adjusts the image's dynamic range to form a broad light-range representation.
  arXiv  Detail & Related papers  (2025-02-28T14:55:37Z)
• LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement [26.585985828583304]
  Low-light image enhancement (LLIE) is a crucial task in computer vision aimed at enhancing the visual fidelity of images captured under low-illumination conditions.<n>We propose LUMINA-Net, an unsupervised deep learning framework that learns adaptive priors from low-light image pairs by integrating multi-stage illumination and reflectance modules.
  arXiv  Detail & Related papers  (2025-02-21T03:37:58Z)
• Dimma: Semi-supervised Low Light Image Enhancement with Adaptive Dimming [0.728258471592763]
  Enhancing low-light images while maintaining natural colors is a challenging problem due to camera processing variations. We propose Dimma, a semi-supervised approach that aligns with any camera by utilizing a small set of image pairs. We achieve that by introducing a convolutional mixture density network that generates distorted colors of the scene based on the illumination differences.
  arXiv  Detail & Related papers  (2023-10-14T17:59:46Z)
• High Dynamic Range and Super-Resolution from Raw Image Bursts [52.341483902624006]
  This paper introduces the first approach to reconstruct high-resolution, high-dynamic range color images from raw photographic bursts captured by a handheld camera with exposure bracketing. The proposed algorithm is fast, with low memory requirements compared to state-of-the-art learning-based approaches to image restoration. Experiments demonstrate its excellent performance with super-resolution factors of up to $times 4$ on real photographs taken in the wild with hand-held cameras.
  arXiv  Detail & Related papers  (2022-07-29T13:31:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.