More than ever before, computers and computation are critical to the image formation process. Across diverse applications and fields, remarkably similar imaging problems appear, requiring sophisticated mathematical, statistical, and algorithmic tools. This conference focuses on imaging as a marriage of computation with physical devices. It emphasizes the interplay between mathematical theory, physical models, and computational algorithms that enable effective current and future imaging systems. Contributions to the conference are solicited on topics ranging from fundamental theoretical advances to detailed system-level implementations and case studies.
Phase unwrapping is an integral part of multiple imaging techniques, and as a result, a wide range of algorithms have been created to unwrap phases. One such algorithm is the minimum Lp-norm phase unwrapping algorithm. This algorithm transforms the phase unwrapping problem into a minimization problem of a certain functional, which it solves with an iterative method. However, the problem is usually not convex, and when there are many sharp edges in the data to be unwrapped, the algorithm often produces a local minimum with new discontinuities in originally smooth areas. To prioritize solutions which minimize the functional better in smooth areas, we use weights to deprioritize data lying along edges in the ground-truth image. This requires a method to find ground-truth edges using the wrapped image, which we describe. When using the modified algorithm, we generally obtain improved results on images with multiple edges (both lower errors and more correct edge placement).
CT images have been used to generate radiation therapy treatment plans for more than two decades. Dual-energy CT (DECT) has shown high accuracy in estimating electronic density or proton stopping power maps for the treatment. However, the presence of metal implants introduces severe striking artifacts in the reconstructed results, affecting the diagnostic accuracy and treatment performance. In order to reduce the metal artifact in DECT, we introduce a metal artifact reduction scheme for iterative DECT algorithms. The corrupt data is substituted with an estimation in each iteration. We utilize normalized metal artifact reduction (NMAR) composed with image domain decomposition to initialize the algorithm and speed up the convergence. A joint statistic DECT algorithm, dual-energy alternating minimization (DEAM), with the proposed scheme is tested on experimental and clinical data acquired on a Philips Big Bore scanner. We compared DEAM with the proposed method to the original DEAM and vendor reconstructions with and without O-MAR. The visualization and quantitative analysis show that DEAM with the proposed method has the best performance in reducing striking artifacts caused by metallic objects.
When an image is captured using an electronic sensor, statistical variations introduced by photon shot and other noise introduce errors in the raw value reported for each pixel sample. Earlier work found that modest improvements in raw image data quality reliably could be obtained by using empirically-determined pixel value error bounds to constrain texture synthesis. However, the prototype software implementation, KREMY (KentuckY Raw Error Modeler, pronounced “creamyâ€), was not effective in processing very noisy images. In comparison, the current work has reimplemented KREMY to make it capable of credibly improving far noisier raw DNG images. The key is a new approach that uses a simpler, but statistical, model for pixel value errors rather than simple bounds constraints.
Noise parameters estimation is needed for many tasks of digital image processing. Many efficient algorithms of noise variance estimation were proposed during last two decades. However, most of those estimators are efficient only for a specific kind of noise for which they were designed. For example, methods of estimation of variance of white additive Gaussian noise (AWGN) fail in the case of additive colored Gaussian noise (ACGN) or for noises with other distributions. In this paper a new fully blind method of noise level estimation is proposed. For a given image, a distorted image with a removed part of pixels (around 10%) is generated. Then an inpainting (or impulse noise removal) method is used to recover missed pixels values. The difference between true and recovered values is used for a robust estimation of noise level. The algorithm is applied for different image scales to estimate noise spectrum. In the paper we propose a convolutional neural network PIXPNet for effective prediction of values of missing pixels. A comparative analysis shows that the proposed PIXPNet provides smallest error of recovered pixels values among all existing methods. A good efficiency of usage of the proposed approach in both AWGN and spatially correlated noise suppression is demonstrated.
Object detection using aerial drone imagery has received a great deal of attention in recent years. While visible light images are adequate for detecting objects in most scenarios, thermal cameras can extend the capabilities of object detection to night-time or occluded objects. As such, RGB and Infrared (IR) fusion methods for object detection are useful and important. One of the biggest challenges in applying deep learning methods to RGB/IR object detection is the lack of available training data for drone IR imagery, especially at night. In this paper, we develop several strategies for creating synthetic IR images using the AIRSim simulation engine and CycleGAN. Furthermore, we utilize an illumination-aware fusion framework to fuse RGB and IR images for object detection on the ground. We characterize and test our methods for both simulated and actual data. Our solution is implemented on an NVIDIA Jetson Xavier running on an actual drone, requiring about 28 milliseconds of processing per RGB/IR image pair.
We measured the contrast of standard charts using two different types of retro-reflectors in an AIRR (Aerial imaging by retro-reflection) system, and examined the results to be reproduced by optical simulation. As a result, it became possible to reproduce the effect of retro-reflector diffraction on the Aerial image quality of the AIRR system by optical simulation.
Deep image denoisers achieve state-of-the-art results but with a hidden cost. As witnessed in recent literature, these deep networks are capable of overfitting their training distributions, causing inaccurate hallucinations to be added to the output and generalizing poorly to varying data. For better control and interpretability over a deep denoiser, we propose a novel framework exploiting a denoising network. We call it controllable confidence-based image denoising (CCID). In this framework, we exploit the outputs of a deep denoising network alongside an image convolved with a reliable filter. Such a filter can be a simple convolution kernel which does not risk adding hallucinated information. We propose to fuse the two components with a frequency-domain approach that takes into account the reliability of the deep network outputs. With our framework, the user can control the fusion of the two components in the frequency domain. We also provide a user-friendly map estimating spatially the confidence in the output that potentially contains network hallucination. Results show that our CCID not only provides more interpretability and control, but can even outperform both the quantitative performance of the deep denoiser and that of the reliable filter, especially when the test data diverge from the training data.
In this paper, a convolutional neural network for joint image demosaicing, denoising, deblurring, super-resolution and clarity enhancement is proposed. The network inputs are four-channel Bayer CFA image (R, G, G, B) and three channels of the same size containing distortions maps, namely, noise level map, blur level map, and clarity degradation map. It is shown that the designed network FiveNet can effectively process images with the mix of five different distortions. It is also demonstrated that adding clarity enhancement into the processing chain can additionally increase image quality (by up to 3-4 dB in PSNR). A small dataset ClarityDegr120 of color images with different clarity degradations and enhancements is designed using images processed by FiveNet. Mean opinion scores (MOS) for the test set are collected. The MOS prove that clarity enhancement can significantly increase image visual quality. A comparative analysis using the MOS demonstrates a low correspondence between image quality metrics and human perception for the clarity enhancement task.
One of the main problems of neural network-based no-reference metrics design for image visual quality assessment is small size of image databases with mean opinion scores (MOS). For large networks which can memorize key features of several thousands of images, usage of the databases for metrics training may lead to overlearning. Since data augmentation for image quality assessment is limited by a horizontal image flipping only, the main way to decrease overlearning is to use transfer learning which can significantly speed up training process. In theis paper, we propose a new technique of transfer learning between networks of different architectures using a large set of images without MOS. We implemented the technique for transfer learning between pre-trained KonCept512 metric and a IMQNet metric proposed in this paper. An effectiveness of the transfer learning is estimated in a numerical analysis. It is shown that the trained IMQNet metric provides significantly better correlation with KonCept512 metric (0.89) than other modern metrics. It is also shown that IMQNet pre-trained by the proposed transfer learning shows better correlation with MOS of KonIQ-10k database (0.86) than IMQNet pre-trained using directly the MOS of KonIQ10k (0.73).