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The CGI (CGI) has the potential of low cost, low dose, and high resolution, which is very attractive for the development of radiation imaging field. However, many sub-coding plates must be used in the modulation process, which greatly…
Image restoration endeavors to reconstruct a high-quality, detail-rich image from a degraded counterpart, which is a pivotal process in photography and various computer vision systems. In real-world scenarios, different types of degradation…
This paper presents a novel network structure with illumination-aware gamma correction and complete image modelling to solve the low-light image enhancement problem. Low-light environments usually lead to less informative large-scale dark…
Accurate characterization of modern on-chip antennas remains challenging, as current probe-station techniques offer limited angular coverage, rely on bespoke hardware, and require frequent manual alignment. This research introduces RAPTAR…
This study presents a practical and dose-efficient strategy for resolution enhancement in planar radiography, based on mechanically supersampled acquisition with high-Z photon-counting detectors (PCDs). Unlike prior event-based or cluster…
We present a compressive radar design that combines multitone linear frequency modulated (LFM) waveforms in the transmitter with a classical stretch processor and sub-Nyquist sampling in the receiver. The proposed compressive illumination…
Ultrasound-modulated optical tomography enables sharp 3D optical imaging of tissues and other turbid media, but the light modulation signals are hard to sensitively measure. A common solution, involving photorefractive crystals, enables the…
This paper presents a method for estimating the resolution of a digital holographic microscope using neural network analysis of reconstructed images. The spectral bandwidth of the source ($\Delta \lambda$) is used as a controlled image…
For diagnosis of shoulder illness, it is essential to look at the morphology deviation of scapula and humerus from the medical images that are acquired from Magnetic Resonance (MR) imaging. However, taking high-resolution MR images is…
We propose an holographic microscopy reconstruction method, which propagates the hologram, in the object half space, in the vicinity of the object. The calibration yields reconstructions with an undistorted reconstruction grid i.e. with…
As Neural Radiance Field (NeRF) implementations become faster, more efficient and accurate, their applicability to real world mapping tasks becomes more accessible. Traditionally, 3D mapping, or scene reconstruction, has relied on expensive…
Precise calibration is a must for high reliance 3D computer vision algorithms. A challenging case is when the camera is behind a protective glass or transparent object: due to refraction, the image is heavily distorted; the pinhole camera…
This work presents a new super-resolution imaging approach by using subwavelength hole resonances. We employ a subwavelength structure in which an array of tiny holes are etched in a metallic slab with the neighboring distance $\ell$ that…
Electron tomography is becoming an increasingly important tool in materials science for studying the three-dimensional morphologies and chemical compositions of nanostructures. The image quality obtained by many current algorithms is…
We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models like LRM that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model…
We propose a novel x-ray imaging system, Multi-Image X-ray Interferometer Module (MIXIM), with which a very high angular resolution can be achieved even with a small system size. MIXIM is composed of equally-spaced multiple slits and an…
We present a new algorithm, called Multiresolution Regularized Expectation Maximization (MREM), for the reconstruction of gamma-ray intensity maps from COMPTEL data. The algorithm is based on the iterative Richardson-Lucy scheme to which we…
Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generator as a deep generative prior, is a promising tool for image…
Autonomous driving algorithms usually employ sRGB images as model input due to their compatibility with the human visual system. However, visually pleasing sRGB images are possibly sub-optimal for downstream tasks when compared to RAW…
High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time. In this paper, a framework called the Fused Attentive Generative Adversarial Networks(FA-GAN)…