Nonuniformity Correction Algorithm Research Articles

Efficient infrared nonuniform correction method considering optical attenuator and integration time

With the increasing number of optical attenuators and integration times in infrared imaging systems, this paper proposes an efficient nonuniform correction method that addresses drawbacks of complex operations and coefficient storage with traditional two-point nonuniform correction methods. First, the efficient nonuniform correction model was derived through the response model of the infrared imaging system with variable attenuators and integration times, which can achieve nonuniform correction of all attenuators and integration times using a correction equation. Based on the model, the efficient nonuniformity correction algorithm using a small number blackbody images was proposed. Finally, the experimental results show that the method has excellent nonuniformity correction performance. Compared with the two-point correction method, the maximum nonuniformity is reduced from 3.7% for the two-point method to 2.2% for the proposed method. In the meantime, the proposed method can greatly reduce the correction time and the number of blackbody temperature sampling points. Moreover, for the infrared imaging system with five integral times and three attenuators in this paper, the two-point method requires 30 correction coefficients, while the proposed method only requires eight correction coefficients. It reduces the required correction coefficients by about three times, achieving efficient and fast nonuniform correction.

Scene-based dual domain non-uniformity correction algorithm for stripe and optics-caused fixed pattern noise removal

Non-uniformity is a long-standing problem that significantly degrades infrared images through fixed pattern noise (FPN). Existing scene-based algorithms for non-uniformity correction (NUC) effectively eliminate stripe FPN assuming consistent inter-frame non-uniformity. However, they are ineffective in handling spatially continuous optical FPN. In this paper, a scene-based dual domain correction approach is proposed to address the non-uniformity problem by simultaneously removing stripe and optics-caused FPN. We achieve this through gain correction in the frequency domain and offset correction in the spatial domain. To remove stripes, we approximate the desired image using a guided filter and iteratively update the bias correction parameters frame by frame. For optics-caused noise removal, we separate low frequency noise from the scene using Fourier transform and update the gain correction parameters accordingly. To mitigate ghost artifacts, a combined strategy is introduced to adaptively adjusts learning rates and weights during the updating stage. Comprehensive evaluations demonstrate that our proposed approach outperforms compared methods in both real and simulated non-uniformity infrared videos.

Non-uniformity correction algorithm for DoFP adapted to integration time variations.

Division of the focal plane (DoFP) polarization detector is a pivotal technology in real-time polarization detection. This technology integrates a micropolarization array (MPA) onto the conventional focal plane, introducing a more intricate non-uniformity than traditional focal plane detectors. Current non-uniformity correction algorithms for DoFP are difficult to adapt to changes in integration time and perform poorly in low-polarization scenarios. Analyzing the characteristics of DoFP, formulating a pixel response model, and introducing an adaptive non-uniformity correction algorithm tailored for varying integration time. The DoFP analysis vectors are decomposed into average polarization response and unit analysis vectors for correction separately to improve the performance of the correction algorithm in different polarization scenarios. The performance of modern correction algorithms was tested and evaluated using standard uniform images, and the proposed method outperformed existing algorithms in terms of polarization measurement accuracy under the root mean square error (RMSE) metric. Moreover, in natural scene images, our proposed algorithm shows favorable visual effects and distinguishes itself from its superior stability amid changes in the integration time.

A Shutter-Less Nonuniformity Correction Algorithm Based on Noise Response Model for TEC-Less Uncooled Infrared Sensors

A Shutter-Less Nonuniformity Correction Algorithm Based on Noise Response Model for TEC-Less Uncooled Infrared Sensors

Strong non-uniformity correction algorithm based on spectral shaping statistics and LMS.

The existence of non-uniformity in infrared detector output images is a widespread problem that significantly degrades image quality. Existing scene-based non-uniformity correction algorithms typically struggle to balance strong non-uniformity correction with scene adaptability. To address this issue, we propose a novel scene-based algorithm that leverages the frequency characteristics of the non-uniformity, combine and improve single-frame stripe removal, multi-scale statistics, and least mean square (LMS) methods. Following the "coarse-to-fine" correction process, the coarse correction stage introduces an adaptive progressive correction strategy based on Laplacian pyramids. By improving 1-D guided filtering and high-pass filtering to shape high-frequency sub-bands, non-uniformity can be well separated from the scene, effectively suppressing ghosting. In the fine correction stage, we optimize the expected image estimation and spatio-temporal adaptive learning rates based on guided filtering LMS method. To validate the efficacy of our algorithm, we conduct extensive simulation and real experiments, demonstrating its adaptability to various scene conditions and its effectiveness in correcting strong non-uniformity.

Visible-Image-Assisted Nonuniformity Correction of Infrared Images Using the GAN with SEBlock

Aiming at reducing image detail loss and edge blur in the existing nonuniformity correction (NUC) methods, a new visible-image-assisted NUC algorithm based on a dual-discriminator generative adversarial network (GAN) with SEBlock (VIA-NUC) is proposed. The algorithm uses the visible image as a reference for better uniformity. The generative model downsamples the infrared and visible images separately for multiscale feature extraction. Then, image reconstruction is achieved by decoding the infrared feature maps with the assistance of the visible features at the same scale. During decoding, SEBlock, a channel attention mechanism, and skip connection are used to ensure that more distinctive channel and spatial features are extracted from the visible features. Two discriminators based on vision transformer (Vit) and discrete wavelet transform (DWT) were designed, which perform global and local judgments on the generated image from the texture features and frequency domain features of the model, respectively. The results are then fed back to the generator for adversarial learning. This approach can effectively remove nonuniform noise while preserving the texture. The performance of the proposed method was validated using public datasets. The average structural similarity (SSIM) and average peak signal-to-noise ratio (PSNR) of the corrected images exceeded 0.97 and 37.11 dB, respectively. The experimental results show that the proposed method improves the metric evaluation by more than 3%.

A Nonuniformity Correction Method Based on 1D Guided Filtering and Linear Fitting for High-Resolution Infrared Scan Images

During imaging, each infrared focal plane linear array scan detector detection unit determines a row of pixels in the image output. This sensor’s nonuniformity appears as horizontal stripes. Correcting nonuniformity in high-resolution images without destroying delicate details is challenging. In this paper, a single-frame-based nonuniformity correction algorithm is proposed. A portion of a single-frame picture is intercepted initially. The 1D column guided filter is applied to smooth the captured image in the vertical direction. Then, the smooth image and high-frequency component with horizontal stripes and texture information are obtained. The subsequent step is to use the smooth portion of the image as the guided image and the high-frequency portion of the image as the input, so that the estimated nonuniformity noise of the image may be extracted using a 1D row guided filter. The segment of the corrected image is then obtained by subtracting the estimated nonuniformity noise from the segment of the raw image. The correction coefficients could be obtained by performing a linear regression fit on the pre- and post-guided filtering image segments. With the correction coefficients, the entire image could be corrected. Based on qualitative and quantitative analysis, the proposed algorithm outperforms other current advanced algorithms in terms of nonuniformity correction and real-time performance.

Improved neural network nonuniformity correction algorithm based on side window filter

Improved neural network nonuniformity correction algorithm based on side window filter

Spatial and temporal adaptive nonuniformity correction for infrared focal plane arrays.

The nonuniformity inherently generated in infrared focal plane arrays (IRFPA) due to the inconsistent offsets of detectors severely degrades the performance of infrared imaging systems. This paper proposes a novel spatial and temporal adaptive nonuniformity correction (NUC) algorithm for the IRFPA, based on a statistical model of the infrared images. After subtracting the local means of an infrared image, the residuals are modeled as a collection of generalized Gaussian random variables with inhomogeneous means. Based on this model, a Maximum Likelihood estimation of the offsets is formally derived, producing an online adaptive temporal filter. The filtering result can be further refined by fusing it with the result of a spatial filter. Therefore, we derive an adaptive Wiener filter to remove the non-uniformity in a single frame and provide an adaptive fusion scheme based on the Minimum Mean Square Error criterion. The overall computational complexity of the proposed NUC algorithm is around O(m n log⁡m n) for an infrared image with the size of m × n, which preserves the potential of the algorithm to be implemented on the board within a thermal camera. Extensive experiments on synthesized and real data have demonstrated the superior performance of the proposed algorithm.

An improved non-uniformity correction algorithm based on calibration

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed

Non-uniformity correction algorithm of space-borne long-wave infrared focal plane arrays based on improved Gamma curve

Non-uniformity correction algorithm of space-borne long-wave infrared focal plane arrays based on improved Gamma curve

Research on sludge concentration detection based on image processing

Sludge concentration is an important indicator of water quality. Based on the different light reflection characteristics of water and sludge, this paper studies the detection of sludge concentration using image processing technology. Combined with MATLAB tools, this paper uses the probability based illumination nonuniformity correction algorithm and adaptive fuzzy threshold segmentation algorithm to simulate and analyze the sewage image, extract the area characteristics and calculate the sludge concentration. At the same time, compared with the real value, it is concluded that the image processing technology can accurately detect the sludge concentration, which lays the foundation for the solid-liquid separation research.

Compensation for the non‐uniformity correction of multi‐channel TDI CCD under variable gain

The compensation for non-uniformity correction of multi-channel time delay integration charge coupled device (TDI CCD) under variable gain is presented. The mathematical model of photoelectric conversion is established first, and, then, a two-step non-uniformity correction method for multi-channel TDI CCD is proposed. Based on this method, a compensation algorithm for non-uniformity correction under variable gain is elaborated in detail. The experimental results show that the proposed algorithm can always maintain the photo response non-uniformity (PRNU) of the corrected images better than 1.14% when the programmable gain amplification (PGA) gain varies within the range of 1–3, compared to the conventional method of degrading to 2.91%. Meanwhile, a compression method for correction coefficients under multiple integral stages is proposed, which can reduce the storage space requirement by 50% at the cost of the PRNU being degraded by a maximum of 0.19% at all integral stages.

Adaptive Multipoint Calibration Non-uniformity Correction Algorithm

Adaptive Multipoint Calibration Non-uniformity Correction Algorithm

Non-uniformity Correction Algorithm for THz Array Detectors in High-Resolution Imaging Applications

One of the major challenges in THz imaging using array detectors is the responsivity non-uniformities across different pixels. This is caused by different pixels responding differently and non-linearly to the same incident field. Unfortunately, such responsivity non-uniformities cannot be corrected by the traditional amplitude normalization method, which only accounts for linear responsivity. Therefore, we propose a pixel calibration scheme that corrects a non-linear responsivity of individual pixels. This calibration scheme is particularly useful in a multi-frame sub-pixel imaging technique, which assumes that all pixels have the same characteristics. Using this imaging technique would allow us to achieve an image pixel size smaller than a detector pixel size, in much faster acquisition time compared with most existing systems employing a single-pixel detector. We demonstrate the performance of our pixel calibration approach through THz imaging of various objects by the multi-frame sub-pixel imaging technique. For a square aluminum piece as a test sample, total variation (TV) in the THz image is reduced by at least 12% after applying the pixel calibration. Finally, we present example uses of our proposed technique in moisture distribution imaging. The proposed technique holds promises towards rapid THz high-resolution imaging in non-destructive testing (NDT) and quality control (QC) applications.

Multiplanar full-field blur correction method for infrared microscopy imaging.

We propose a 3D full-field focusing method for microscopic mid-wave infrared (MWIR) imagery. The method is based on the experimental estimation of a confined volumetric vision microscope point spread function. The technique employs our well-known constant-range-based nonuniformity correction algorithm as a preprocessing step and then an iteration in the z-axis Fourier-based deconvolution. The technique's ability to compensate for localized blur is demonstrated using two different real MWIR microscopic video sequences, captured from two microscopic living organisms using a Janos-Sofradir MWIR microscopy setup. The performance of the proposed algorithm is assessed on real and simulated noisy infrared data by computing the root-mean-square error and the roughness Laplacian pattern indexes, which are specifically developed for the present work.

Infrared stripe correction algorithm based on wavelet decomposition and total variation-guided filtering

Stripe non-uniformity severely affects the quality of infrared images. It is challenging to remove stripe noise in low-texture images without blurring the details. We propose a single-frame image stripe correction algorithm that removes infrared noise while preserving image details. Firstly, wavelet transform is used for multi-scale analysis of the image. At the same time, Total variation model is used for small window to smooth the original image. The small-scale total variation model can well preserve the edge information of the image, but it will leave stripe noise. Therefore, according to the prior knowledge of the vertical component of the stripe noise, the spatial filtering is finally performed: the smoothed image is used as the guide image for the stripe noise denoising. It is possible to prevent the lead filter from mistaking the strong stripe noise as edge detail, resulting in corrected image residual streak noise. The algorithm is systematically evaluated by experiments on simulated images and original infrared images, as well as compared with the current advanced infrared stripe non-uniformity correction algorithms. It is proved that our algorithm can better eliminate stripe noise and preserve edge details.

Single-frame-based column fixed-pattern noise correction in an uncooled infrared imaging system based on weighted least squares.

Uncooled infrared images typically suffer from column fixed-pattern noise (FPN), which is introduced by the non-uniformity of the column-parallel readout circuit. It is a challenging task to remove column FPN without introducing stripe artifacts near strong vertical edges. In this paper, we introduce a novel single-frame-based algorithm to accurately correct column FPN. The algorithm contains two 1D filters. First, a 1D weighted least-squares filter is applied to perform edge-preserving filtering in the horizontal direction to obtain a coarse estimation of the clear scene. Then, a local weighted ridge regression is performed between the horizontal smoothed image and the raw infrared image in vertical direction to refine this estimation. Through an analysis on the cause of stripe artifacts, the proposed non-uniformity correction (NUC) algorithm is proved to be effective in eliminating this problem. The performance of our proposed algorithm is thoroughly investigated and compared to four state-of-the-art single-frame-based destriping methods using both simulated and real experiments.

Single-reference-based solution for two-point nonuniformity correction of infrared focal plane arrays

Among numerous nonuniformity correction (NUC) algorithms, two-point calibration-based NUC is the most effective and commonly-used one for scientific and commercial infrared imagers. However, conventional two-point NUC requires two references at different level of flux, which is sometimes difficult to achieve in field applications. To overcome the restriction, a single-reference-based solution for two-point nonuniformity correction of infrared focal plane arrays is proposed in this paper. A single reference, which is easy to obtain, and a spherical reflecting warm shield are required to conduct this NUC method. The design method of this warm shield is developed. Several NUC experiments were performed to validate the proposed method. The experimental results indicate that the proposed NUC method yields almost equivalent performance with the conventional two-point NUC method, moreover it is economical and convenient in practical applications.

Temporal-Spatial Nonlinear Filtering for Infrared Focal Plane Array Stripe Nonuniformity Correction

In this work, we introduce a temporal-spatial approach for infrared focal plane array (IRFPA) stripe nonuniformity correction in infrared images that generates visually appealing results. We posit that the nonuniformity appears as a striped structure in the spatial domain and that the pixel values change slowly in the temporal domain. Based on this, we formulate our correction method in two steps. In the first step, weighted guided image filtering with our adaptive weight is utilized to predict the stripe nonuniformity using a single frame. In the second step, the temporal profile of each pixel can be formed using a few frames of successive nonuniformity images. Further, we present a temporal nonlinear diffusion equation to remove scene residuals from the temporal profile of nonuniformity images in order to estimate a more accurate value of the stripe nonuniformity. The results of extensive experiments demonstrate that the proposed nonuniformity correction algorithm substantially outperforms many state-of-the-art approaches, including both traditional and deep convolution-neural-network-based methods, on four popular infrared videos. In addition, the proposed method only requires a fraction (less than ten) of the video frames.