Abstract
The problem of stripe non-uniformity in array-based infrared imaging systems has been the focus of many research studies. Among the proposed correction techniques, total variation models have been proven to significantly reduce the effect of this type of noise on the captured image. However, they also cause the loss of some image details and textures due to over-smoothing effect. In this paper, a correction scheme is proposed based on unidirectional variation model to exploit the direction characteristic of the stripe noise, in which an edge-aware weighting is incorporated to convey image structure retaining ability to the overall algorithm. Moreover, a statistical-based regularization is also introduced to further enhance correction performance around strong edges. The proposed approach is thoroughly scrutinized and compared to the state-of-the-art de-striping techniques using real stripe non-uniform images. Results demonstrate a significant improvement in edge preservation with better correction performance.
Highlights
Spatial non-uniformity continues to represent a major downside in Focal Plane Arrays (FPA)—based infrared imaging systems
Total variation approach for non-uniformity correction falls in the category of solving an inverse problem where the estimated true scene is deduced from the degraded image
Some bright edges that appear in the estimated noise (Figure 6a) can be clearly seen. These edges significantly differ from the noise, which make it easy for the statistical regularization to extract them and remove them from the estimated noise (Figure 6b). This can be done without affecting the correction, since these strong edges are usually not affected by the stripe fixed pattern noise (FPN)
Summary
Spatial non-uniformity continues to represent a major downside in Focal Plane Arrays (FPA)—based infrared imaging systems. The third category and most relevant to the present work is the optimization-based approach [13,14,15], where the correction process aims to estimate the corrected image by minimizing a cost function that mutually ensures the correction for stripe FPN along with the preservation of image details. Under these methods the cost function is usually formed by two terms, the first one responsible for removing the stripe noise called the “regularization term” and the second one helps to preserve the detail information during the correction called the “fidelity term”.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
