Dependent component analysis for blind restoration of images degraded by turbulent atmosphere

Abstract

In our previous research, we applied independent component analysis (ICA) for the restoration of image sequences degraded by atmospheric turbulence. The original high-resolution image and turbulent sources were considered independent sources from which the degraded image is composed of. Although the result was promising, the assumption of source independence may not be true in practice. In this paper, we propose to apply the concept of dependent component analysis (DCA), which can relax the independence assumption, to image restoration. In addition, the restored image can be further enhanced by employing a recently developed Gabor-filter-bank-based single channel blind image deconvolution algorithm. Both simulated and real data experiments demonstrate that DCA outperforms ICA, resulting in the flexibility in the use of adjacent image frames. The contribution of this research is to convert the original multi-frame blind deconvolution problem into blind source separation problem without 3 the assumption on source independence; as a result, there is no a priori information, such as sensor bandwidth, point-spread-function, or statistics of source images, that is required.

Analytical and mathematical methods for revealing hidden details in ancient manuscripts and paintings: A review

“…One option in this case could be to rely on dependent component analysis (DCA), a class of model-based techniques that exploit other possible properties of sources or mixtures to reach their goal [72], [73]. This type of technique has been applied extensively to fields such as remote sensing [74], [75] and medical imaging [76]. Although some DCA approaches could be employed to analyse different types of cultural heritage-related images, only one proposal is present in the open literature in which a DCA approach is used for the digital restoration of colour images of double-sided documents [77].…”