Enhancement of Underwater Video Mosaics for Post-Processing

Mosaics of seafloor created from still images or video acquired underwater have proved to be useful for construction of maps of forensic and archeological sites, species\u27 abundance estimates, habitat characterization, etc. Images taken by a camera mounted on a stable platform are registered (at first pair-wise and then globally) and assembled in a high resolution visual map of the surveyed area. While this map is usually sufficient for a human orientation and even quantitative measurements, it often contains artifacts that complicate an automatic post-processing (for example, extraction of shapes for organism counting, or segmentation for habitat characterization). The most prominent artifacts are inter-frame seams caused by inhomogeneous artificial illumination, and local feature misalignments due to parallax effects - result of an attempt to represent a 3D world on a 2D map. In this paper we propose two image processing techniques for mosaic quality enhancement - median mosaic-based illumination correction suppressing appearance of inter-frame seams, and micro warping decreasing influence of parallax effects

Assessing electron beam sensitivity for SrTiO3 and La0.7Sr0.3MnO3 using electron energy loss spectroscopy

Thresholds for beam damage have been assessed for La0.7Sr0.3MnO3 and SrTiO3 as a function of electron probe current and exposure time at 80 and 200 kV acceleration voltage. The materials were exposed to an intense electron probe by aberration corrected scanning transmission electron microscopy (STEM) with simultaneous acquisition of electron energy loss spectroscopy (EELS) data. Electron beam damage was identified by changes of the core loss fine structure after quantification by a refined and improved model based approach. At 200 kV acceleration voltage, damage in SrTiO3 was identified by changes both in the EEL fine structure and by contrast changes in the STEM images. However, the changes in the STEM image contrast as introduced by minor damage can be difficult to detect under several common experimental conditions. No damage was observed in SrTiO3 at 80 kV acceleration voltage, independent of probe current and exposure time. In La0.7Sr0.3MnO3, beam damage was observed at both 80 and 200 kV acceleration voltages. This damage was observed by large changes in the EEL fine structure, but not by any detectable changes in the STEM images. The typical method to validate if damage has been introduced during acquisitions is to compare STEM images prior to and after spectroscopy. Quantifications in this work show that this method possibly can result in misinterpretation of beam damage as changes of material properties

Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications

Robust Principal Component Analysis (RPCA) via rank minimization is a powerful tool for recovering underlying low-rank structure of clean data corrupted with sparse noise/outliers. In many low-level vision problems, not only it is known that the underlying structure of clean data is low-rank, but the exact rank of clean data is also known. Yet, when applying conventional rank minimization for those problems, the objective function is formulated in a way that does not fully utilize a priori target rank information about the problems. This observation motivates us to investigate whether there is a better alternative solution when using rank minimization. In this paper, instead of minimizing the nuclear norm, we propose to minimize the partial sum of singular values, which implicitly encourages the target rank constraint. Our experimental analyses show that, when the number of samples is deficient, our approach leads to a higher success rate than conventional rank minimization, while the solutions obtained by the two approaches are almost identical when the number of samples is more than sufficient. We apply our approach to various low-level vision problems, e.g. high dynamic range imaging, motion edge detection, photometric stereo, image alignment and recovery, and show that our results outperform those obtained by the conventional nuclear norm rank minimization method.Comment: Accepted in Transactions on Pattern Analysis and Machine Intelligence (TPAMI). To appea