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  1. Image security is becoming an increasingly important issue due to advances in deep learning based image manipulations, such as deep image inpainting and deepfakes. There has been considerable work to date on detecting such image manipulations using improved algorithms, with little attention paid to the possible role that hardware advances may have for improving security. We propose to use a focal stack camera as a novel secure imaging device, to the best of our knowledge, that facilitates localizing modified regions in manipulated images. We show that applying convolutional neural network detection methods to focal stack images achieves significantly better detection accuracy compared to single image based forgery detection. This work demonstrates that focal stack images could be used as a novel secure image file format and opens up a new direction for secure imaging.

    Free, publicly-accessible full text available May 2, 2023
  2. This paper discusses algorithms for phase retrieval where the measurements follow independent Poisson distributions. We developed an optimization problem based on maximum likelihood estimation (MLE) for the Poisson model and applied Wirtinger flow algorithm to solve it. Simulation results with a random Gaussian sensing matrix and Poisson measurement noise demonstrated that the Wirtinger flow algorithm based on the Poisson model produced higher quality reconstructions than when algorithms derived from Gaussian noise models (Wirtinger flow, Gerchberg Saxton) are applied to such data, with significantly improved computational efficiency.
  3. The recent trend in regularization methods for inverse problems is to replace handcrafted sparsifying operators with datadriven approaches. Although using such machine learning techniques often improves image reconstruction methods, the results can depend significantly on the learning methodology. This paper compares two supervised learning methods. First, the paper considers a transform learning approach and, to learn the transform, introduces a variant on the Procrustes method for wide matrices with orthogonal rows. Second, we consider a bilevel convolutional filter learning approach. Numerical experiments show the learned transform performs worse for denoising than both the handcrafted finite difference transform and the learned filters, which perform similarly. Our results motivate the use of bilevel learning.
  4. Abstract

    Recent years have seen the rapid growth of new approaches to optical imaging, with an emphasis on extracting three-dimensional (3D) information from what is normally a two-dimensional (2D) image capture. Perhaps most importantly, the rise of computational imaging enables both new physical layouts of optical components and new algorithms to be implemented. This paper concerns the convergence of two advances: the development of a transparent focal stack imaging system using graphene photodetector arrays, and the rapid expansion of the capabilities of machine learning including the development of powerful neural networks. This paper demonstrates 3D tracking of point-like objects with multilayer feedforward neural networks and the extension to tracking positions of multi-point objects. Computer simulations further demonstrate how this optical system can track extended objects in 3D, highlighting the promise of combining nanophotonic devices, new optical system designs, and machine learning for new frontiers in 3D imaging.

  5. Deep learning (DL) has been increasingly explored in low-dose CT image denoising. DL products have also been submitted to the FDA for premarket clearance. While having the potential to improve image quality over the filtered back projection method (FBP) and produce images quickly, generalizability of DL approaches is a major concern because the performance of a DL network can depend highly on the training data. In this work we take a residual encoder-decoder convolutional neural network (REDCNN)-based CT denoising method as an example. We investigate the effect of the scan parameters associated with the training data on the performance of this DL-based CT denoising method and identifies the scan parameters that may significantly impact its performance generalizability. This abstract particularly examines these three parameters: reconstruction kernel, dose level and slice thickness. Our preliminary results indicate that the DL network may not generalize well between FBP reconstruction kernels, but is insensitive to slice thickness for slice-wise denoising. The results also suggest that training with mixed dose levels improves denoising performance.
  6. Convolutional operator learning is gaining attention in many signal processing and computer vision applications. Learning kernels has mostly relied on so-called patch-domain approaches that extract and store many overlapping patches across training signals. Due to memory demands, patch-domain methods have limitations when learning kernels from large datasets – particularly with multi-layered structures, e.g., convolutional neural networks – or when applying the learned kernels to high-dimensional signal recovery problems. The so-called convolution approach does not store many overlapping patches, and thus overcomes the memory problems particularly with careful algorithmic designs; it has been studied within the “synthesis” signal model, e.g., convolutional dictionary learning. This paper proposes a new convolutional analysis operator learning (CAOL) framework that learns an analysis sparsifying regularizer with the convolution perspective, and develops a new convergent Block Proximal Extrapolated Gradient method using a Majorizer (BPEG-M) to solve the corresponding block multi-nonconvex problems. To learn diverse filters within the CAOL framework, this paper introduces an orthogonality constraint that enforces a tight-frame filter condition, and a regularizer that promotes diversity between filters. Numerical experiments show that, with sharp majorizers, BPEG-M significantly accelerates the CAOL convergence rate compared to the state-of-the-art block proximal gradient (BPG) method. Numerical experiments for sparse-view computationalmore »tomography show that a convolutional sparsifying regularizer learned via CAOL significantly improves reconstruction quality compared to a conventional edge-preserving regularizer. Using more and wider kernels in a learned regularizer better preserves edges in reconstructed images.« less