Scene-based Nonuniformity Correction Method Research Articles

Scene-Based Nonuniformity Correction Method Using Principal Component Analysis for Infrared Focal Plane Arrays

In this paper, principal component analysis is introduced to form a scene-based nonuniformity correction method for infrared focal plane arrays. The estimation of the gain and offset of the infrared detector and the correction of nonuniformity based on the neural network method with a novel estimation of desired target value are achieved concurrently. The current frame and several adjacent registered frames are decomposed onto a set of principal components, and then the first principal component is extracted to construct the desired target value. It is practical, forms fewer ghosting artifacts, and considerably promotes correction precision. Numerical experiments demonstrate that the proposed method presents excellent performance when dealing with clean infrared data with synthetic pattern noise as well as the real infrared video sequence.

Infrared scene-based non-uniformity correction based on deep learning model

In this paper, an infrared scene-based non-uniformity correction method based on deep learning technology has been proposed. This method combines the scene-based infrared non-uniformity correction with the state-of-the-art deep learning technology. The traditional scene-based non-uniformity correction technologies generally face the problem that when the radiation of the scene is changed, the correction parameters may not converge anymore, and the correction process will rise again. Multiple times of correction will potentially increase the risk of the correction failure. The deep learning can help setting up a systematic correction parameter which self-adaptive to the thermal imager, which means that once the parameters are computed, the non-uniformity will be corrected according to the deep learning network by itself. Although the pre-calculation period of setting up the parameters take much time, the upcoming correction process is much easier than the traditional technologies. We use the feed-forward denoising convolutional network as the fundamental structure, and deploy the modified residual learning process as well as the batch normalization process onto it. Figures and charts show the priority of our method.

A Median-Ratio Scene-Based Non-Uniformity Correction Method for Airborne Infrared Point Target Detection System.

Infrared detectors suffer from severe non-uniform noise which highly reduces image resolution and point target signal-to-noise ratio. This is the restriction for airborne point target detection systems in reaching the background limit. The existing methods are either not accurate enough, or too complex to be applied to engineering. To improve the precision and reduce the algorithm complexity of scene-based Non-Uniformity Correction (NUC) for an airborne point target detection system, a Median-Ratio Scene-based NUC (MRSBNUC) method is proposed. The method is based on the assumption that the median value of neighboring pixels is approximately constant. The NUC coefficients are calculated recursively by selecting the median ratio of adjacent pixels. Several experiments were designed and conducted. For both the clear sky scene and scene with clouds, the non-uniformity is effectively reduced. Furthermore, targets were detected in outfield experiments. For Target 1 48.36 km away and Target 2 50.53 km away, employing MRSBNUC the SNR of the target increased 2.09 and 1.73 times respectively compared to Two-Point NUC. It was concluded that the MRSBNUC method can reduce the non-uniformity of the detector effectively which leads to a longer detection distance and fewer false alarms of the airborne point target detection system.

Statistical Scene-Based Non-Uniformity Correction Method with Interframe Registration.

The non-uniform response in infrared focal plane array (IRFPA) detectors inevitably produces corrupted images with a fixed-pattern noise. In this paper, we present a novel and adaptive scene-based non-uniformity correction (NUC) method called Correction method with Statistical scene-based and Interframe Registration (CSIR), which realizes low delay calculation of correction coefficient for infrared image. This method combines the statistical method and registration method to achieve a better NUC performance. Specifically, CSIR estimates the gain coefficient with statistical method to give registration method an appropriate initial value. This combination method not only reduces the need of interactive pictures, which means lower time delay, but also achieves better performance compared to the statistical method and other single registration methods. To verify this, real non-uniformity infrared image sequences collected by ourselves were used, and the advantage of CSIR was compared thoroughly on frame number (corresponding to delay time) and accuracy. The results show that the proposed method could achieve a significantly fast and reliable fixed-pattern noise reduction with the effective gain and offset.

A Novel Infrared Focal Plane Non-Uniformity Correction Method Based on Co-Occurrence Filter and Adaptive Learning Rate

Non-uniformity commonly exists in the infrared focal plane, which behaves as the fixed pattern noise (FPN) and seriously affects the image quality of the infrared imaging system. This paper proposed a novel scene-based non-uniformity correction method with a new edge-preserve filter and adaptive learning rate. First, using co-occurrence filter as the desired image estimation, the proposed method removed the FPN while preserving the image details. Then, an adaptive learning rate connected with both temporal motion and spatial correlation factor is utilized to decrease the effect of ghosting artifacts. In this way, the proposed method overcomes the shortcomings of the traditional scene-based non-uniformity. Several real infrared image sequences collected in different conditions are used to verify the performance of the proposed method. The experimental results demonstrate that the proposed method has a much better visual effect, making a great balance between the non-uniformity correction and details preservation. Compared with other good NUC methods, this method also has better performance in the aspects of applicability and robustness, which has great application value.

FPN estimation based nonuniformity correction for infrared imaging system

In this paper, to compensate for the fixed pattern noise (FPN) of infrared imaging system, a novel scene-based nonuniformity correction method is proposed based on FPN estimation. The FPN is supposed to be equal to the offset of detectors in IRFPA and is estimated based on the temporal filtering results of adjacent pixel difference. The temporal filters we used are the one order lag filter and mean filter. At the same time, the threshold constraint is introduced to select the candidate pixels of the original images for the estimation of FPN. This leads to the less interference of the details in scenes and the temporarily still scenes, thus our proposed method performs well on the actual scene. Besides this, the low computational complexity, simplicity of implementation and good real-time performance make our proposed method more suitable for hardware implementation. Experiments are conducted with three infrared video sequences to evaluate the performance of our proposed method and other two related methods, the correction results and quality indexes show that the performance of our proposed method is the best among these three methods.

Interior Radiation Noise Reduction Method Based on Multiframe Processing in Infrared Focal Plane Arrays Imaging System

Infrared focal plane arrays imaging system tend to be affected by the interior thermal radiation that drifts slowly over the time, resulting in the continuous spatial low-frequency nonuniformity noise in the image. In this paper, a scene-based nonuniformity correction method for interior thermal radiation is proposed. The gradient domain least mean square (LMS) algorithm is used to estimate the gradient caused by the interior radiation noise according to the fixed spatial low-frequency information in multiframe images. After that, the calibration parameters are obtained by fitting the derivatives of the polynomial-based correction model with the calculated gradient image. The performance of the proposed method is thoroughly studied with infrared image sequences with simulated nonuniformity and real nonuniformity. The results show that the proposed algorithm can remove interior radiation noise more completely without ghosting artifacts than the state-of-art algorithms and is more robust to different scenarios.

A novel scene-based non-uniformity correction method for SWIR push-broom hyperspectral sensors

A novel scene-based non-uniformity correction (NUC) method for short-wavelength infrared (SWIR) push-broom hyperspectral sensors is proposed and evaluated. This method relies on the assumption that for each band there will be ground objects with similar reflectance to form uniform regions when a sufficient number of scanning lines are acquired. The uniform regions are extracted automatically through a sorting algorithm, and are used to compute the corresponding NUC coefficients. SWIR hyperspectral data from airborne experiment are used to verify and evaluate the proposed method, and results show that stripes in the scenes have been well corrected without any significant information loss, and the non-uniformity is less than 0.5%. In addition, the proposed method is compared to two other regular methods, and they are evaluated based on their adaptability to the various scenes, non-uniformity, roughness and spectral fidelity. It turns out that the proposed method shows strong adaptability, high accuracy and efficiency.

Scene-based nonuniformity correction for airborne point target detection systems.

Images acquired by airborne infrared search and track (IRST) systems are often characterized by nonuniform noise. In this paper, a scene-based nonuniformity correction method for infrared focal-plane arrays (FPAs) is proposed based on the constant statistics of the received radiation ratios of adjacent pixels. The gain of each pixel is computed recursively based on the ratios between adjacent pixels, which are estimated through a median operation. Then, an elaborate mathematical model describing the error propagation, derived from random noise and the recursive calculation procedure, is established. The proposed method maintains the characteristics of traditional methods in calibrating the whole electro-optics chain, in compensating for temporal drifts, and in not preserving the radiometric accuracy of the system. Moreover, the proposed method is robust since the frame number is the only variant, and is suitable for real-time applications owing to its low computational complexity and simplicity of implementation. The experimental results, on different scenes from a proof-of-concept point target detection system with a long-wave Sofradir FPA, demonstrate the compelling performance of the proposed method.

Guided filter and adaptive learning rate based non-uniformity correction algorithm for infrared focal plane array

Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.

A time-domain projection-based registration-scene-based nonuniformity correction technology and its detailed hardware realization

Non-uniformity (NU) in infrared images can cause great degradation of the image quality and appearance. Scene-based nonuniformity correction (SBNUC) has become a very effective way to deal with NU. Although many SBNUC methods have been developed by researchers worldwide, few of them have a good correction performance and can be applied to small-package real-time devices. In this paper, we propose a time-domain projection-based registrationscene-based NU correction technology and its detailed hardware realization. We developed a new projection estimator to calculate the relative displacement of neighboring frames. The estimator uses a column and row projection vector to calculate the displacement separately without reducing the accuracy. We also developed an improved gain coefficient correction method using the corrected bias coefficient to correct the gain coefficient by clarifying the intrinsic relationship between these two coefficients instead of correcting them separately. We have also thoroughly analyzed how this technology performs with an actual infrared video sequence containing both low-frequency and high-frequency NUs. The hardware realization of this technology in a single-FPGA-core real-time system is also described in detail. We have successfully realized this technology in a real engineering application. Detailed flow charts for the hardware implementation of this algorithm are also provided.

Scene-based Nonuniformity Correction Algorithm Based on Temporal Median Filter

Scene-based nonuniformity correction techniques for infrared focal-plane arrays have been widely considered as a key technology, and various algorithms have been proposed to compensate for fixed-pattern noise. However, the existed algorithms' capability is always restricted by the problems of convergence speed and ghosting artifacts. In this paper, an effective scene-based nonuniformity correction method is proposed to solve these problems. The algorithm is an improvement over the constant statistics method and a temporal median is utilized with the Gaussian kernel to estimate the nonuniformity parameters. Also theoretical analysis is conducted to demonstrate that effective ghosting artifacts elimination and superior convergence speed can be obtained with the proposed method. Finally, the performance of the proposed technique is tested with infrared image sequences with simulated nonuniformity and with infrared imagery with real nonuniformity. The results show the proposed method is able to estimate each detector's gain and to offset reliably and that it performs better in increasing convergence speed and reducing ghosting artifacts compared with the conventional techniques.

An Enhanced Scene-Based NUC Method for PMMW Focal Plane Array Imaging

In this paper, an enhanced scene-based NUC (Non-Uniformity Correction) method that consists of rough correction and accurate correction is proposed for PMMW-FPA (Passive Millimeter-Wave Focal-Plane Array) imaging. In this method, electromagnetic radiation uniformity of a scene is a critical component needed for rough correction, and a neural network method is utilized to make accurate correction. The proposed method prevails over the neural network method in NUC performance and convergence speed as well as detail information keeping. Experimental results completed with simulated data as well as measured data demonstrate the effectiveness of the proposed method.

Scene-based nonuniformity correction method using multiscale constant statistics

In scene-based nonuniformity correction (NUC) methods for infrared focal plane array cameras, the statistical approaches have been well studied because of their lower computational complexity. However, when the assumptions imposed by statistical algorithms are violated, their performance is poor. Moreover, many of these techniques, like the global constant statistics method, usually need tens of thousands of image frames to obtain a good NUC result. In this paper, we introduce a new statistical NUC method called the multiscale constant statistics (MSCS). The MSCS statically considers that the spatial scale of the temporal constant distribution expands over time. Under the assumption that the nonuniformity is distributed in a higher spatial frequency domain, the spatial range for gain and offset estimates gradually expands to guarantee fast compensation for nonuniformity. Furthermore, an exponential window and a tolerance interval for the acquired data are introduced to capture the drift in nonuniformity and eliminate the ghosting artifacts. The strength of the proposed method lies in its simplicity, low computational complexity, and its good trade-off between convergence rate and correction precision. The NUC ability of the proposed method is demonstrated by using infrared video sequences with both synthetic and real nonuniformity.

Total variation approach for adaptive nonuniformity correction in focal-plane arrays

In this Letter we propose an adaptive scene-based nonuniformity correction method for fixed-pattern noise removal in imaging arrays. It is based on the minimization of the total variation of the estimated irradiance, and the resulting function is optimized by an isotropic total variation approach making use of an alternating minimization strategy. The proposed method provides enhanced results when applied to a diverse set of real IR imagery, accurately estimating the nonunifomity parameters of each detector in the focal-plane array at a fast convergence rate, while also forming fewer ghosting artifacts.

Scene-based nonuniformity correction and enhancement: pixel statistics and subpixel motion

We propose a framework for scene-based nonuniformity correction (NUC) and nonuniformity correction and enhancement (NUCE) that is required for focal-plane array-like sensors to obtain clean and enhanced-quality images. The core of the proposed framework is a novel registration-based nonuniformity correction super-resolution (NUCSR) method that is bootstrapped by statistical scene-based NUC methods. Based on a comprehensive imaging model and an accurate parametric motion estimation, we are able to remove severe/structured nonuniformity and in the presence of subpixel motion to simultaneously improve image resolution. One important feature of our NUCSR method is the adoption of a parametric motion model that allows us to (1) handle many practical scenarios where parametric motions are present and (2) carry out perfect super-resolution in principle by exploring available subpixel motions. Experiments with real data demonstrate the efficiency of the proposed NUCE framework and the effectiveness of the NUCSR method.