Parameter Blending for Multi-Camera Harmonization for Automotive Surround View Systems

• URL: http://arxiv.org/abs/2406.11066v1
• Date: Sun, 16 Jun 2024 20:43:50 GMT
• Title: Parameter Blending for Multi-Camera Harmonization for Automotive Surround View Systems
• Authors: Yuzhuo Ren, Yining Deng, David Pajak, Robin Jenkin, Niranjan Avadhanam, Varsha Hedau,
• Abstract summary: We propose harmonization algorithm which applies before stitching to adjust multiple cameras' color and tone. Our proposed algorithm outperforms global color transfer method in both visual quality and computational cost.
• Score: 4.693433639736405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In a surround view system, the image color and tone captured by multiple cameras can be different due to cameras applying auto white balance (AWB), global tone mapping (GTM) individually for each camera. The color and brightness along stitched seam location may look discontinuous among multiple cameras which impacts overall stitched image visual quality. To improve the color transition between adjacent cameras in stitching algorithm, we propose harmonization algorithm which applies before stitching to adjust multiple cameras' color and tone so that stitched image has smoother color and tone transition between adjacent cameras. Our proposed harmonization algorithm consists of AWB harmonization and GTM harmonization leveraging Image Signal Processor (ISP)'s AWB and GTM metadata statistics. Experiment result shows that our proposed algorithm outperforms global color transfer method in both visual quality and computational cost.

Related papers

• Learning Camera-Agnostic White-Balance Preferences [30.282495562510285]
  Post-illuminant-estimation mapping transforms neutral illuminant corrections into preferred corrections in a camera-agnostic space.<n>Our proposed model is lightweight -- containing only $sim$500 parameters -- and runs in just 0.024 milliseconds on a typical flagship mobile CPU.
  arXiv  Detail & Related papers  (2025-07-02T04:11:01Z)
• Multi-Spectral Gaussian Splatting with Neural Color Representation [8.200719250787651]
  We present MS-Splatting -- a multi-spectral 3D Gaussian Splatting framework.<n>Our method does not require cross-modal camera calibration.<n>It is versatile enough to model a variety of different spectra, including thermal and near-infra red.
  arXiv  Detail & Related papers  (2025-06-03T21:36:50Z)
• CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis [75.25966323298003]
  Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding.<n> variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies.<n>We introduce $textbfCARL$, a model for $textbfC$amera-$textbfA$gnostic $textbfR$esupervised $textbfL$ across RGB, multispectral, and hyperspectral imaging modalities.
  arXiv  Detail & Related papers  (2025-04-27T13:06:40Z)
• Discovering an Image-Adaptive Coordinate System for Photography Processing [51.164345878060956]
  We propose a novel algorithm, IAC, to learn an image-adaptive coordinate system in the RGB color space before performing curve operations. This end-to-end trainable approach enables us to efficiently adjust images with a jointly learned image-adaptive coordinate system and curves.
  arXiv  Detail & Related papers  (2025-01-11T06:20:07Z)
• Redundancy-Aware Camera Selection for Indoor Scene Neural Rendering [54.468355408388675]
  We build a similarity matrix that incorporates both the spatial diversity of the cameras and the semantic variation of the images. We apply a diversity-based sampling algorithm to optimize the camera selection. We also develop a new dataset, IndoorTraj, which includes long and complex camera movements captured by humans in virtual indoor environments.
  arXiv  Detail & Related papers  (2024-09-11T08:36:49Z)
• Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation [109.1912721224697]
  We present the Unified Frequency-Assisted transFormer framework, named UFAFormer, to address the DGM4 problem. By leveraging the discrete wavelet transform, we decompose images into several frequency sub-bands, capturing rich face forgery artifacts. Our proposed frequency encoder, incorporating intra-band and inter-band self-attentions, explicitly aggregates forgery features within and across diverse sub-bands.
  arXiv  Detail & Related papers  (2023-09-18T11:06:42Z)
• Multi-channel Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoising [9.20787253404652]
  One traditional strategy is to convert the RGB image to a less correlated color space and denoise each channel of the new space separately. This paper proposes a new multi-channel optimization model for color image denoising under the nuclear norm minus Frobenius norm minimization framework. Experimental results on both synthetic and real noise datasets demonstrate the proposed model outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2022-09-16T04:10:29Z)
• Cross-Camera Deep Colorization [10.254243409261898]
  We propose an end-to-end convolutional neural network to align and fuse images from a color-plus-mono dual-camera system. Our method consistently achieves substantial improvements, i.e., around 10dB PSNR gain.
  arXiv  Detail & Related papers  (2022-08-26T11:02:14Z)
• FRIH: Fine-grained Region-aware Image Harmonization [49.420765789360836]
  We propose a novel global-local two stages framework for Fine-grained Region-aware Image Harmonization (FRIH) Our algorithm achieves the best performance on iHarmony4 dataset (PSNR is 38.19 dB) with a lightweight model.
  arXiv  Detail & Related papers  (2022-05-13T04:50:26Z)
• Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild [159.71025525493354]
  We propose a trainable Image Signal Processing framework that produces DSLR quality images given RAW images captured by a smartphone. To address the color misalignments between training image pairs, we employ a color-conditional ISP network and optimize a novel parametric color mapping between each input RAW and reference DSLR image.
  arXiv  Detail & Related papers  (2022-03-20T20:13:59Z)
• Mitigating Channel-wise Noise for Single Image Super Resolution [33.383282898248076]
  We propose to super-resolve noisy color images by considering the color channels jointly. Results demonstrate the super-resolving capability of the approach in real scenarios.
  arXiv  Detail & Related papers  (2021-12-14T17:45:15Z)
• Color Mapping Functions For HDR Panorama Imaging: Weighted Histogram Averaging [31.415704979877557]
  It is challenging to stitch multiple images with different exposures due to possible color distortion and loss of details in the brightest and darkest regions of input images. In this paper, a novel color mapping algorithm is first proposed by introducing a new concept of weighted histogram averaging (WHA) The proposed WHA algorithm also preserves details in the brightest and darkest regions of the histogram images.
  arXiv  Detail & Related papers  (2021-11-14T09:10:32Z)
• Auto White-Balance Correction for Mixed-Illuminant Scenes [52.641704254001844]
  Auto white balance (AWB) is applied by camera hardware to remove color cast caused by scene illumination. This paper presents an effective AWB method to deal with such mixed-illuminant scenes. Our method does not require illuminant estimation, as is the case in traditional camera AWB modules.
  arXiv  Detail & Related papers  (2021-09-17T20:13:31Z)
• High-Resolution Depth Maps Based on TOF-Stereo Fusion [27.10059147107254]
  We propose a novel TOF-stereo fusion method based on an efficient seed-growing algorithm. We show that the proposed algorithm outperforms 2D image-based stereo algorithms. The algorithm potentially exhibits real-time performance on a single CPU.
  arXiv  Detail & Related papers  (2021-07-30T15:11:42Z)
• Probabilistic Color Constancy [88.85103410035929]
  We define a framework for estimating the illumination of a scene by weighting the contribution of different image regions. The proposed method achieves competitive performance, compared to the state-of-the-art, on INTEL-TAU dataset.
  arXiv  Detail & Related papers  (2020-05-06T11:03:05Z)

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.