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Efficient single image non-uniformity correction algorithm

Yohann Tendero, Jerome Gilles, Stephane Landeau, Jean-Michel Morel

TL;DR

The obtained single image method works on static images, and therefore requires no registration, no camera motion compensation, and no closed aperture sensor equalization, and is landscape independent.

Abstract

This paper introduces a new way to correct the non-uniformity (NU) in uncooled infrared-type images. The main defect of these uncooled images is the lack of a column (resp. line) time-dependent cross-calibration, resulting in a strong column (resp. line) and time dependent noise. This problem can be considered as a 1D flicker of the columns inside each frame. Thus, classic movie deflickering algorithms can be adapted, to equalize the columns (resp. the lines). The proposed method therefore applies to the series formed by the columns of an infrared image a movie deflickering algorithm. The obtained single image method works on static images, and therefore requires no registration, no camera motion compensation, and no closed aperture sensor equalization. Thus, the method has only one camera dependent parameter, and is landscape independent. This simple method will be compared to a state of the art total variation single image correction on raw real and simulated images. The method is real time, requiring only two operations per pixel. It involves no test-pattern calibration and produces no "ghost artifacts".

Efficient single image non-uniformity correction algorithm

TL;DR

The obtained single image method works on static images, and therefore requires no registration, no camera motion compensation, and no closed aperture sensor equalization, and is landscape independent.

Abstract

This paper introduces a new way to correct the non-uniformity (NU) in uncooled infrared-type images. The main defect of these uncooled images is the lack of a column (resp. line) time-dependent cross-calibration, resulting in a strong column (resp. line) and time dependent noise. This problem can be considered as a 1D flicker of the columns inside each frame. Thus, classic movie deflickering algorithms can be adapted, to equalize the columns (resp. the lines). The proposed method therefore applies to the series formed by the columns of an infrared image a movie deflickering algorithm. The obtained single image method works on static images, and therefore requires no registration, no camera motion compensation, and no closed aperture sensor equalization. Thus, the method has only one camera dependent parameter, and is landscape independent. This simple method will be compared to a state of the art total variation single image correction on raw real and simulated images. The method is real time, requiring only two operations per pixel. It involves no test-pattern calibration and produces no "ghost artifacts".

Paper Structure

This paper contains 14 sections, 10 equations, 10 figures.

Table of Contents

  1. Introduction
  2. Anterior work
  3. Midway infrared correction
  4. The midway histogram equalization method
  5. The idea
  6. Midway Infrared Equalization (MIRE)
  7. Implementation
  8. Quality analysis
  9. Experiments
  10. Total variation based method
  11. Comparative experiments
  12. Discussion and conclusion
  13. Future work
  14. Appendix.

Figures (10)

  • Figure 1: Two histograms $h_1$, $h_2$ (left side) and the corresponding midway histogram $h$ (on the right), compared to the direct histogram average, which would create two modes and is therefore wrong.
  • Figure 2: Computation time for various sizes and quantifications in seconds (using Matlab). This time could be made to real with any standard processor.
  • Figure 3: Image 1 : The groundtruth (left) the simulated FPN (right, RMSE=0.1932).
  • Figure 4: The TV based method (left, RMSE=0.1817), MIRE (right, RMSE=0.1715).
  • Figure 5: Top left : RAW (cooled camera), top right : TV based method, at the bottom : MIRE.
  • ...and 5 more figures