Multimodal Imaging of Cotton Wool Spots in Branch Retinal Vein Occlusion.

PURPOSE: To describe and follow cotton wool spots (CWS) in branch retinal vein occlusion (BRVO) using multimodal imaging. METHODS: In this prospective cohort study including 24 patients with new-onset BRVO, CWS were described and analyzed in color fundus photography (CF), spectral domain optical coherence tomography (SD-OCT), infrared (IR) and fluorescein angiography (FA) every 3 months for 3 years. The CWS area on SD-OCT and CF was evaluated using OCT-Tool-Kit software: CWS were marked in each single OCT B-scan and the software calculated the area by interpolation. RESULTS: 29 central CWS lesions were found. 100% of these CWS were visible on SD-OCT, 100% on FA and 86.2% on IR imaging, but only 65.5% on CF imaging. CWS were visible for 12.4 ± 7.5 months on SD-OCT, for 4.4 ± 3 months and 4.3 ± 3.4 months on CF and on IR, respectively, and for 17.5 ± 7.1 months on FA. The evaluated CWS area on SD-OCT was larger than on CF (0.26 ± 0.17 mm(2) vs. 0.13 ± 0.1 mm(2), p < 0.0001). The CWS area on SD-OCT and surrounding pathology such as intraretinal cysts, avascular zones and intraretinal hemorrhage were predictive for how long CWS remained visible (r(2) = 0.497, p < 0.002). CONCLUSIONS: The lifetime and presentation of CWS in BRVO seem comparable to other diseases. SD-OCT shows a higher sensitivity for detecting CWS compared to CF. The duration of visibility of CWS varies among different image modalities and depends on the surrounding pathology and the CWS size.

A Logarithmic Image Prior for Blind Deconvolution

Blind Deconvolution consists in the estimation of a sharp image and a blur kernel from an observed blurry image. Because the blur model admits several solutions it is necessary to devise an image prior that favors the true blur kernel and sharp image. Many successful image priors enforce the sparsity of the sharp image gradients. Ideally the L0 “norm” is the best choice for promoting sparsity, but because it is computationally intractable, some methods have used a logarithmic approximation. In this work we also study a logarithmic image prior. We show empirically how well the prior suits the blind deconvolution problem. Our analysis confirms experimentally the hypothesis that a prior should not necessarily model natural image statistics to correctly estimate the blur kernel. Furthermore, we show that a simple Maximum a Posteriori formulation is enough to achieve state of the art results. To minimize such formulation we devise two iterative minimization algorithms that cope with the non-convexity of the logarithmic prior: one obtained via the primal-dual approach and one via majorization-minimization.

Bilayer Blind Deconvolution with the Light Field Camera

In this paper we propose a solution to blind deconvolution of a scene with two layers (foreground/background). We show that the reconstruction of the support of these two layers from a single image of a conventional camera is not possible. As a solution we propose to use a light field camera. We demonstrate that a single light field image captured with a Lytro camera can be successfully deblurred. More specifically, we consider the case of space-varying motion blur, where the blur magnitude depends on the depth changes in the scene. Our method employs a layered model that handles occlusions and partial transparencies due to both motion blur and out of focus blur of the plenoptic camera. We reconstruct each layer support, the corresponding sharp textures, and motion blurs via an optimization scheme. The performance of our algorithm is demonstrated on synthetic as well as real light field images.

A Clearer Picture of Total Variation Blind Deconvolution

Blind deconvolution is the problem of recovering a sharp image and a blur kernel from a noisy blurry image. Recently, there has been a significant effort on understanding the basic mechanisms to solve blind deconvolution. While this effort resulted in the deployment of effective algorithms, the theoretical findings generated contrasting views on why these approaches worked. On the one hand, one could observe experimentally that alternating energy minimization algorithms converge to the desired solution. On the other hand, it has been shown that such alternating minimization algorithms should fail to converge and one should instead use a so-called Variational Bayes approach. To clarify this conundrum, recent work showed that a good image and blur prior is instead what makes a blind deconvolution algorithm work. Unfortunately, this analysis did not apply to algorithms based on total variation regularization. In this manuscript, we provide both analysis and experiments to get a clearer picture of blind deconvolution. Our analysis reveals the very reason why an algorithm based on total variation works. We also introduce an implementation of this algorithm and show that, in spite of its extreme simplicity, it is very robust and achieves a performance comparable to the top performing algorithms.