Stripe Nonuniformity Correction Research Articles

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.

A new method of relative radiometric calibration for hyperspectral imaging based on skylight monitor

A new method of relative radiometric calibration based on the skylight monitor is presented for airborne hyperspectral remote sensing to eliminate the negative effect on data resulting from the time-varying skylight during a long flight. In terms of large area operation of mapping or spectral imaging, a static radiation model hardly reveals actual cases as external environment keeps changing. Compared with common considerable factors, fluctuation of skylight is the primary one that may observably affect spectral imaging continuously leading to radiation changes upon flight lines. To get rid of the influence of skylight on spectral imagery and monitoring the variation during the flight, we assemble a set of skylight monitor fixed to the aircraft cabin for the sake of recording the semi-spherical skylight keeping synchronization with hyperspectral imaging system and the position and orientation system. The skylight monitor consists of a cosine corrector on the top receiving the skylight, a stable optical fiber and a visible and near-infrared linear array modular spectrometer. The corresponding matched data of skylight with each frame of images is used to correct related digital number after stripe non-uniformity correction, achieving mean relative standard deviation (coefficient of variation or CV) of under 6% for all 256 bands. The final result shows a satisfactory calibrated image of an appropriate evaluation criteria with contrast of the original image.

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.

Local spatial correlation-based stripe non-uniformity correction algorithm for single infrared images

Stripe non-uniformity typically exists in infrared images and affects the visual effect; thus, eliminating stripe non-uniformity is essential to improve image quality. In this paper, a correction model with higher accuracy is developed. Unlike some other stripe non-uniformity correction methods using the same gain coefficient and offset parameters for the pixels of each column, different deviations of the correction parameters in the same column resulted by unsatisfactory preliminary non-uniformity correction are considered and are thought to be small and still relevant in space. The proposed method calculates the correction parameters for each pixel respectively based on the intrinsic spatial correlation between adjacent pixels in a column. What is more, an edge detection method is included. The experimental results indicate that the proposed algorithm effectively eliminates stripe noise of images of different scenes and it also works well in terms of preserving details. Furthermore, the algorithm exhibits high real-time performance.

Single Infrared Image-Based Stripe Nonuniformity Correction via a Two-Stage Filtering Method.

The presence of stripe nonuniformity severely degrades the image quality and affects the performance in many infrared (IR) sensing applications. Prior works correct the nonuniformity by using similar spatial representations, which inevitably damage some detailed structures of the image. In this paper, we instead take advantage of spectral prior of stripe noise to solve its correction problem in single IR image. We first analyse the significant spectral difference between stripes and image structures and utilize this knowledge to characterize stripe nonuniformity. Then a two-stage filtering strategy is adopted combining spectral and spatial filtering. The proposed method enables stripe nonuniformity to be eliminated from coarse to fine, thus preserving image details well. Extensive experiments on simulated images and raw IR images demonstrate that the proposed method achieves superior correction performance over the recent state-of-the-art methods.

Stripe nonuniformity correction for infrared imaging system based on single image optimization

Infrared imaging is often disturbed by stripe nonuniformity noise. Scene-based correction method can effectively reduce the impact of stripe noise. In this paper, a stripe nonuniformity correction method based on differential constraint is proposed. Firstly, the gray distribution of stripe nonuniformity is analyzed and the penalty function is constructed by the difference of horizontal gradient and vertical gradient. With the weight function, the penalty function is optimized to obtain the corrected image. Comparing with other single-frame approaches, experiments show that the proposed method performs better in both subjective and objective analysis, and does less damage to edge and detail. Meanwhile, the proposed method runs faster. We have also discussed the differences between the proposed idea and multi-frame methods. Our method is finally well applied in hardware system.

Iteratively reweighted unidirectional variational model for stripe non-uniformity correction

In this paper, we propose an adaptive unidirectional variational nonuniformity correction algorithm for fixed-pattern noise removal. The proposed algorithm is based on a unidirectional variational sparse model that makes use of unidirectional characteristics of stripe nonuniformity noise. The iteratively reweighted least squares (IRLS) technique is introduced to optimize the proposed correction model, which makes the proposed algorithm easy to implement with existing conjugate gradient method without introducing additional variables and parameters. Moreover, we derive a formula to automatically update the regularization parameter from the images. Comparative experimental results on real infrared images indicate that the proposed method can remove the stripe nonuniformity noise effectively while maintaining more useful image details.

基于自适应扩散模型的单帧红外条纹非均匀性校正算法

基于自适应扩散模型的单帧红外条纹非均匀性校正算法

Single image stripe nonuniformity correction with gradient-constrained optimization model for infrared focal plane arrays

Stripe non-uniformity commonly exists in infrared focal plane array (IRFPA). The current study develops a scene-based stripe non-uniformity correction (NUC) method using only a single image. Efforts were made to translate the stripe NUC problem into a gradient-constrained optimization problem. The optimization aims to seek the optimal image with a vertical gradient as close to the original image as possible and make the energy of the horizontal gradient as small as possible. The experiments indicate that the proposed algorithm can provide enhanced results effectively and efficiently.

Efficient single image stripe nonuniformity correction method for infrared focal plane arrays

We present a novel, stripe nonuniformity correction algorithm for infrared focal plane arrays. This method relies on the separation of nonuniformity and true scene, and the nonuniformity correction parameter is obtained by traversing the error function of two adjacent columns’ pixels in local template window. Based on the succession of two adjacent columns’ correlation, the stripe nonuniformity correction can be achieved in a single frame. Experimental results, to illustrate the performance of the method, include the use of infrared image sequences with simulated nonuniformity and a diverse set of real IR imagery.

Stripe nonuniformity correction based on registration for infrared-focal plane arrays

Stripe nonuniformity is a very typical fixed pattern noise in line infrared focal plane arrays(IR-FPA) and uncooled staring IR-FPA.Its origination was analyzed.A stripe nonuniformity correction algorithm based on brightness constancy assumption and registration was proposed.Brightness mean-square error function was obtained according to adjacent frames.Nonuniformity correction parameters could be derived through the unique global optimum by minimizing the function.Experimental results indicate that the proposed algorithm could achieve a great convergence in several frames with low computational complexity and enhance the effective of stripe nonuniformity correction.

Minimum mean square error method for stripe nonuniformity correction

Stripe nonuniformity is very typical in line infrared focal plane (IRFPA) and uncooled starring IRFPA. We develop the minimum mean square error (MMSE) method for stripe nonuniformity correction (NUC). The goal of the MMSE method is to determine the optimal NUC parameters for making the corrected image the closest to the ideal image. Moreover, this method can be achieved in one frame, making it more competitive than other scene-based NUC algorithms. We also demonstrate the calibration results of our algorithm using real and virtual infrared image sequences. The experiments verify the positive effect of our algorithm.

Correction method for stripe nonuniformity

Stripe nonuniformity is very typical in line infrared focal plane arrays (IR-FPA) and uncooled staring IR-FPA. In this paper, the mechanism of the stripe nonuniformity is analyzed, and the gray-scale co-occurrence matrix theory and optimization theory are studied. Through these efforts, the stripe nonuniformity correction problem is translated into the optimization problem. The goal of the optimization is to find the minimal energy of the image's line gradient. After solving the constrained nonlinear optimization equation, the parameters of the stripe nonuniformity correction are obtained and the stripe nonuniformity correction is achieved. The experiments indicate that this algorithm is effective and efficient.