Non-uniformity Correction of Infrared Images by Midway Equalization

Abstract

The non-uniformity is a time-dependent noise caused by the lack of sensor equalization. We present here the detailed algorithm and online demo of the non-uniformity correction method by midway infrared equalization. This method was designed to suit infrared images. Nevertheless, it can be applied to images produced for example by scanners, or by push-broom satellites. This single image method works on static images, is fully automatic, has no user parameter, and requires no registration. It needs no camera motion compensation, no closed aperture sensor equalization and is able to correct for a fully non-linear non-uniformity. Source Code The source code, version 2.0, is available from the article web page 1. The documentation is included in the archive. Basic compilation and usage instructions are included in the README.txt file. The source code for the contrast adjustment preprocessing is available from the same location with its own documentation (see [10]). The demo permits to try the proposed method on several well chosen test images, and on any uploaded image. To improve visibility (without changing the algorithm) the input image can be preprocessed using the "Simplest Color Balance" algorithm [10]. The s1 parameter is the percentage of pixels saturated to black and s2 the percentage saturated to white. If both s1 = s2 = 0 the image is simply stretched to [0, 255] by an affine contrast change (causing no loss of information) before applying the denoising algorithm. The output image is re-stretched to [0, 255] by an affine contrast change. The outputs are 1) the processed image and 2) the optimal scale parameter s found by the algorithm.

Robust Approach for Nonuniformity Correction in Infrared Focal Plane Array

“…Since the real FPN used here is a stripe nonuniformity, we also compared our method to one of the state-of-the-art methods that deal with this kind of nonuniformity, namely the midway infrared equalization (MIRE) algorithm [21]. This approach is a single frame correction that exploits the midway infrared equalization technique to equalize the histogram of each column (or line) using the midway of the histograms of the neighboring columns, hence the name MIRE.…”

Strip non-uniformity correction in uncooled long-wave infrared focal plane array based on noise source characterization

“…Given a strip-noise corrupted 6. Testing the NL-means denoising algorithm [9] (the middle image) and our proposed (the right image) strip non-uniformity removal algorithms on a noisy infrared image [19]. Table 1 AP-RMSE of input images, outputs using MHE [12], and output using our proposed method.…”

“…[12] (the second column images) and using our method (the third column images). The input images of (a)-(d) are from [19] and the input images of (e)-(f) are our own captured infrared images.…”

Dark Current Compensation and Sensitivity Adjustment on Gallium Arsenide Linear Array Detector for X-Ray Imaging

“…Images described below were obtained with 3 modules which have minimum pixel intensity fluctuation due to scanner array movement and defects in input channels of ASIC which affects on output signals. Raw image example with compensated dark currents is showed in Figure 6 together with adjusted sensitivity by using the ramp object calibration and by MIRE [14] algorithm. The scan was done from top to the bottom and the exposure time was 1.4 seconds.…”