Quantitative assessment of restoration quality in blind deconvolution is not straightforward. The intricacy comes from the opposite shifts occurring during image reconstruction in both the reconstructed image and point-spread function. State of the art procedures attempting alignment lack specificity and might actually induce interpolation-based errors, whereas alignment-free approaches disregard the possibility of shift variability. Other methods introduce a superresolution-based MSE/PSNR measure involving non-integer shifts. We propose a method to estimate non-integer displacements between the ground-truth image and the reconstructed image via the Fourier domain, which enables at the same time to incorporate interpolation-free shift compensation into the computation of MSE/PSNR measures. We tested our method on synthetically shifted images with additive noise as well as deconvolution results with known shift. Results indicate that distortions of error measures are reduced compared to interpolation-based shift compensation methods, getting closer to a fair assessment of restoration quality without wrongly favouring any blind deconvolution method over another. It was observed that PSNR measures corrected with estimated displacements are close to those corrected with real displacements.

Reflecting objects such as tea pots and glasses, but also diffusely reflecting objects such as a user's shirt, can be used to spy on confidential data displayed on a monitor. First, we show how reflections in the user's eye can be exploited for spying on confidential data. Second, we investigate to what extent monitor images can be reconstructed from the diffuse reflections on a wall or the user's clothes, and provide information-theoretic bounds limiting this type of attack. Third, we evaluate the effectiveness of several countermeasures. This substantially improves previous work (Backes et al., IEEE Symposium on Security & Privacy, 2008).