Adaptive Image-Registration-Based Nonuniformity Correction Algorithm With Ghost Artifacts Eliminating for Infrared Focal Plane Arrays

Abstract

In this paper, we present a new adaptive image registration nonuniformity correction method with the function of eliminating ghost artifacts. This method assumes that the irradiation of objects stays unchanged during the adjacent two frames' time interval and then corrects the corresponding pixels of two frames by the result of image registration. With regard to real-time continuous image sequence, we first calculate the displacement vectors based on their row and column projections. Then, by bidirectional image registration, we can get the overlapped area matrices of two frames accurately. Meanwhile, a variance threshold is set to judge the scene classification, and then, displacement revising is selectively added to decrease stripe noise. Considering that moving targets exist, the error function for updating is also accordingly adapted to eliminate the ghost artifacts. By using the least-mean-square-error iteration algorithm, the gain and offset coefficients of the overlapped area can be updated in real time with the image sequence adaptively. The advantages of this algorithm lie in its high efficiency of image processing, low computational complexity, and few ghost artifacts. A clean infrared image sequence and two real infrared datasets with nonuniformity are used to compare the ability and correction performance of different algorithms. The results show that our algorithm performs better than other compared algorithms and obtains almost no ghost artifacts.

Survey on Some Scene Based Nonuniformity Correction Algorithms for Infrared Images

“…These methods' basic function is to minimize ghosting artifacts which comes due to spatial fixed pattern noise in infrared images. We have referred NN-NUC from [3], CS-NUC from [4], IR-NUC from [5], and GE-NUC from [6] A. NN-NUC This method uses new adaptive learning rate rule into the rough calculation of gain and offset of each detector. This learning rate is choosing for it will depend on the spatial variation of the read-out data of one frame, and also it will depend on the temporal motion between two successive frames.…”

“…In this section, we take comparison of the respected scene based methods. We compare them by referring the results demonstrated in [6]. They compare the results with clean infrared sequence.…”

“…The paper divided into four sections 2 nd section contains detail information of the respected scene based nonuniformity correction methods and 3 rd section contains comparison of the methods. To compare these methods we refer an example from [6] to visualize the changes found in the images. And we can verify the better method for denoising.…”

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

“…Afterward, the registration-based methods were proposed, and they assume that the response of each detector to the same scene is identical and the difference is on account of the nonuniformity. Hayat first proposed the registration-based NUC method [18] and some scholars also proposed several methods by extending this kind of methods [19,20]. These methods need fewer frames of an image than the statistical-based methods and have almost no ghosting artifacts.…”

Nonuniformity Correction for Variable-Integration-Time Infrared Camera

“…However, its complex algorithm prevents the implementation of hardware and real-time processing, thus most scene-based correction methods are studied in laboratory. Ghosting artifacts is another limitation for practical application of these methods [16]. An algebraic scene-based algorithm is proposed in [19] for bias nonuniformity correction, which is computationally efficient and easily implemented compared to other scene-based algorithms.…”