IEEE Transactions on Pattern Analysis and Machine Intelligence最新文献

In real-life passive non-line-of-sight (NLOS) imaging there is an overwhelming amount of undesired scattered radiance, called clutter, that impedes reconstruction of the desired NLOS scene. This paper explores using the spectral domain of the scattered light field to separate the desired scattered radiance from the clutter. We propose two techniques: The first separates the multispectral scattered radiance into a collection of objects each with their own uniform color. The objects which correspond to clutter can then be identified and removed based on how well they can be reconstructed using NLOS imaging algorithms. This technique requires very few priors and uses off-the-shelf algorithms. For the second technique, we derive and solve a convex optimization problem assuming we know the desired signal's spectral content. This method is quicker and can be performed with fewer spectral measurements. We demonstrate both techniques using realistic scenarios. In the presence of clutter that is 50 times stronger than the desired signal, the proposed reconstruction of the NLOS scene is 23 times more accurate than typical reconstructions and 5 times more accurate than using the leading clutter rejection method.

Learning-based image reconstruction models, such as those based on the U-Net, require a large set of labeled images if good generalization is to be guaranteed. In some imaging domains, however, labeled data with pixel- or voxel-level label accuracy are scarce due to the cost of acquiring them. This problem is exacerbated further in domains like medical imaging, where there is no single ground truth label, resulting in large amounts of repeat variability in the labels. Therefore, training reconstruction networks to generalize better by learning from both labeled and unlabeled examples (called semi-supervised learning) is problem of practical and theoretical interest. However, traditional semi-supervised learning methods for image reconstruction often necessitate handcrafting a differentiable regularizer specific to some given imaging problem, which can be extremely time-consuming. In this work, we propose "supervision by denoising" (SUD), a framework to supervise reconstruction models using their own denoised output as labels. SUD unifies stochastic averaging and spatial denoising techniques under a spatio-temporal denoising framework and alternates denoising and model weight update steps in an optimization framework for semi-supervision. As example applications, we apply SUD to two problems from biomedical imaging-anatomical brain reconstruction (3D) and cortical parcellation (2D)-to demonstrate a significant improvement in reconstruction over supervised-only and ensembling baselines. Our code available at https://github.com/seannz/sud.

We propose that spaceborne polarimetric imagers can be calibrated, or self-calibrated using zodiacal light (ZL). ZL is created by a cloud of interplanetary dust particles. It has a significant degree of polarization in a wide field of view. From space, ZL is unaffected by terrestrial disturbances. ZL is insensitive to the camera location, so it is suited for simultaneous cross-calibration of satellite constellations. ZL changes on a scale of months, thus being a quasi-constant target in realistic calibration sessions. We derive a forward model for polarimetric image formation. Based on it, we formulate an inverse problem for polarimetric calibration and self-calibration, as well as an algorithm for the solution. The methods here are demonstrated in simulations. Towards these simulations, we render polarized images of the sky, including ZL from space, polarimetric disturbances, and imaging noise.