Infrared imagery has been used in many areas such as military, surveillance, medical imaging and numerous industrial branches. The recent increase in the use of infrared (IR) imaging techniques in various fields draws the attention of more studies towards this area. One of the common problems of the thermal IR detector units is the existence of bad pixels. Bad pixels may arise from various reasons such as manufacturing processes or operating conditions. This phenomenon is commonly dealt with well-known calibration methods. However, they are generally applied at factory level or they interrupt the operational use due to the need for utilizing a uniform reference scene. For those reasons recent methods employ scene based approaches without requiring a special equipment. Those types of methods commonly make some assumptions based on statistical characteristics of bad pixels. They mainly assume that bad pixels deviate at a certain level from their neighboring pixels. They rely on sufficient variation of scene content in time and the fact that possible false detections can be canceled out due to scene variation. Nevertheless, this assumption does not always hold, especially when the camera is stationary. In such cases, some distinctive parts of the underlying scene may be falsely regarded as bad pixels. To that end, we develop a method that is able to isolate the scene content from bad pixels in order to eliminate erroneous detections of scene parts. The proposed method benefits from the motion of the camera which provides responses of different pixels for the same scene region. From this information, we expect similar responses for the registered pixels, if they are not defective. On the other hand, if the pixel responses are exceedingly different, then we can deduce that the corresponding pixel may be defective. For this purpose, we first register adjacent frames using an efficient 1D projection based matching method. To ensure a more robust registration, we use edge maps rather than the intensity image. After the registration of two frames, we construct an error map for the overlapping regions of the two frames. We declare our candidate defective pixels by assessing the deviation levels of error values. Candidate pixels are accumulated across non-stationary frames to obtain temporally consistent detections. Since our inter-frame registration step provides motion information, we avoid accumulation when camera is stationary. We also prevent erroneous registrations by checking for the sufficient scene detail. The performance evaluations are carried out on an extensive dataset consisting of real thermal camera images. The dataset contains a wide variety of scene content and various scenarios featuring stationary camera conditions that causes failures in traditional statistical variation based approaches. The results of our experiments are assessed in terms of true and false bad pixel detections as compared to ground-truth bad pixel labellings. The results show that the proposed inter-frame registration based bad pixel detection method achieves successful results without any assumption about scene content and any additional reference surface.
We propose a novel scene-based non-uniformity correction method to achieve better fixed-pattern noise reduction and eliminate ghosting artifacts. Our approach is based on robust parameter updates via inter-frame registration and spatio-temporally consistent correction coefficients. We utilized a GMM-based spatio-temporal update mask to selectively refine the estimations of correction coefficients. The results of our experiments on an extensive dataset consisting of both synthetically corrupted data and real infrared videos show that the proposed algorithm achieves superior performance in PSNR and roughness metrics with notably lower ghosting artifacts when compared to other state-of-the-art methods.
In the design of pilot helmets with night vision capability, to not limit or block the sight of the pilot, a transparent visor is used. The reflected image from the coated part of the visor must coincide with the physical human sight image seen through the nonreflecting regions of the visor. This makes the alignment of the visor halves critical. In essence, this is an alignment problem of two optical parts that are assembled together during the manufacturing process. Shack–Hartmann wavefront sensor is commonly used for the determination of the misalignments through wavefront measurements, which are quantified in terms of the Zernike polynomials. Although the Zernike polynomials provide very useful feedback about the misalignments, the corrective actions are basically ad hoc. This stems from the fact that there exists no easy inverse relation between the misalignment measurements and the physical causes of the misalignments. This study aims to construct this inverse relation by making use of the expressive power of the neural networks in such complex relations. For this purpose, a neural network is designed and trained in MATLAB® regarding which types of misalignments result in which wavefront measurements, quantitatively given by Zernike polynomials. This way, manual and iterative alignment processes relying on trial and error will be replaced by the trained guesses of a neural network, so the alignment process is reduced to applying the counter actions based on the misalignment causes. Such a training requires data containing misalignment and measurement sets in fine detail, which is hard to obtain manually on a physical setup. For that reason, the optical setup is completely modeled in Zemax® software, and Zernike polynomials are generated for misalignments applied in small steps. The performance of the neural network is experimented and found promising in the actual physical setup.
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