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Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation.

Min Seok Lee1, Sang Wook Park2, Moon Gi Kang3

  • 1School of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul 03722, Korea. gasinamul@empal.com.

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|May 31, 2017
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Summary

This study introduces a novel spatio-spectral-temporal filter for denoising Color Filter Array (CFA) sequences. The filter effectively reduces noise by considering inter-channel correlations, improving image quality from CCD/CMOS sensors.

Keywords:
color filter array image sensorinter-channel correlationspatial-temporal filtervideo denoising

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Area of Science:

  • Image processing
  • Computer vision
  • Sensor technology

Background:

  • Color Filter Array (CFA) sequences from CCD/CMOS sensors suffer from noise due to under-sampled patterns.
  • Inter-channel correlation is crucial for effective denoising in CFA data.
  • Existing denoising methods often struggle with spatial resolution degradation and motion artifacts.

Purpose of the Study:

  • To propose a novel spatio-spectral-temporal filter for denoising CFA sequences.
  • To address the challenge of inter-channel correlation in CFA denoising.
  • To improve the quality of denoised CFA sequences without introducing motion artifacts.

Main Methods:

  • A spatio-spectral-temporal filter is developed, integrating spatial, spectral, and temporal domains.
  • Nonlocal means (NLM) spatial filtering with patch-based difference (PBD) refinement considers intra- and inter-channel correlations.
  • A motion-compensated temporal filter with inter-channel correlated motion estimation and compensation is employed.
  • A motion adaptive detection value dynamically adjusts the spatial and temporal filter contributions.

Main Results:

  • The proposed filter effectively denoises CFA sequences by leveraging spatio-tempo-spectral correlations.
  • Experimental results demonstrate superior performance compared to state-of-the-art methods on both simulated and real data.
  • The method overcomes spatial resolution degradation and avoids motion artifacts in the denoised sequences.

Conclusions:

  • The proposed spatio-spectral-temporal filter offers a robust solution for CFA sequence denoising.
  • Considering inter-channel correlation is vital for enhancing denoising performance.
  • The developed framework achieves excellent objective and subjective results, outperforming existing techniques.