Beam-Shape Effects and Noise Removal from THz Time-Domain Images in
Reflection Geometry in the 0.25-6 THz Range
- URL: http://arxiv.org/abs/2203.00417v1
- Date: Tue, 1 Mar 2022 13:15:50 GMT
- Title: Beam-Shape Effects and Noise Removal from THz Time-Domain Images in
Reflection Geometry in the 0.25-6 THz Range
- Authors: Marina Ljubenovic, Alessia Artesani, Stefano Bonetti, and Arianna
Traviglia
- Abstract summary: This paper focuses on a procedure aimed at reducing the degradation effects, frequency-dependent blur and noise, in Terahertz Time-Domain Spectroscopy (THz- TDS) images in reflection geometry.
It describes the application of a joint deblurring and denoising approach that had been previously proved to be effective for the restoration of THz- TDS images in transmission geometry.
The study demonstrates the ability of image processing to restore data in the 0.25 - 6 THz range, spanning over more than four octaves.
- Score: 2.379911867541422
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The increasing need of restoring high-resolution Hyper-Spectral (HS) images
is determining a growing reliance on Computer Vision-based processing to
enhance the clarity of the image content. HS images can, in fact, suffer from
degradation effects or artefacts caused by instrument limitations. This paper
focuses on a procedure aimed at reducing the degradation effects,
frequency-dependent blur and noise, in Terahertz Time-Domain Spectroscopy
(THz-TDS) images in reflection geometry. It describes the application of a
joint deblurring and denoising approach that had been previously proved to be
effective for the restoration of THz-TDS images in transmission geometry, but
that had never been tested in reflection modality. This mode is often the only
one that can be effectively used in most cases, for example when analyzing
objects that are either opaque in the THz range, or that cannot be displaced
from their location (e.g., museums), such as those of cultural interest.
Compared to transmission mode, reflection geometry introduces, however, further
distortion to THz data, neglected in existing literature. In this work, we
successfully implement image deblurring and denoising of both uniform-shape
samples (a contemporary 1 Euro cent coin and an inlaid pendant) and samples
with the uneven reliefs and corrosion products on the surface which make the
analysis of the object particularly complex (an ancient Roman silver coin). The
study demonstrates the ability of image processing to restore data in the 0.25
- 6 THz range, spanning over more than four octaves, and providing the
foundation for future analytical approaches of cultural heritage using the
far-infrared spectrum still not sufficiently investigated in literature.
Related papers
- Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement [71.13353154514418]
Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge.
We present a novel Mamba scanning mechanism, called RAWMamba, to effectively handle raw images with different CFAs.
We also present a Retinex Decomposition Module (RDM) grounded in Retinex prior, which decouples illumination from reflectance to facilitate more effective denoising and automatic non-linear exposure correction.
arXiv Detail & Related papers (2024-09-11T06:12:03Z) - Harnessing Optical Imaging Limit through Atmospheric Scattering Media [5.435475238868005]
We introduce a comprehensive model that incorporates contributions from target characteristics, atmospheric effects, imaging system, digital processing, and visual perception.
The model allows to reevaluate the effectiveness of conventional imaging recording, processing, and perception.
An immediate application of the study is the extension of the image range by an amount of 1.2 times with noise reduction via multi-frame averaging.
arXiv Detail & Related papers (2024-04-23T14:31:44Z) - Towards High-quality HDR Deghosting with Conditional Diffusion Models [88.83729417524823]
High Dynamic Range (LDR) images can be recovered from several Low Dynamic Range (LDR) images by existing Deep Neural Networks (DNNs) techniques.
DNNs still generate ghosting artifacts when LDR images have saturation and large motion.
We formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition.
arXiv Detail & Related papers (2023-11-02T01:53:55Z) - A Conditional Denoising Diffusion Probabilistic Model for Radio
Interferometric Image Reconstruction [4.715025376297672]
We present VIC-DDPM, a Visibility and Image Conditioned Denoising Diffusion Probabilistic Model.
Our main idea is to use both the original visibility data in the spectral domain and dirty images in the spatial domain to guide the image generation process with DDPM.
Our results show that our method significantly improves the resulting images by reducing artifacts, preserving fine details, and recovering dim sources.
arXiv Detail & Related papers (2023-05-16T03:00:04Z) - Making the Invisible Visible: Toward High-Quality Terahertz Tomographic
Imaging via Physics-Guided Restoration [24.045067900801072]
Terahertz (THz) tomographic imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection.
We propose a novel multi-view Subspace-guided Restoration Network (SARNet) that fuses multi-viewAttention and multi-spectral features of THz images for effective image restoration and 3D tomographic reconstruction.
arXiv Detail & Related papers (2023-04-28T15:05:46Z) - Near-filed SAR Image Restoration with Deep Learning Inverse Technique: A
Preliminary Study [5.489791364472879]
Near-field synthetic aperture radar (SAR) provides a high-resolution image of a target's scattering distribution-hot spots.
Meanwhile, imaging result suffers inevitable degradation from sidelobes, clutters, and noises.
To restore the image, current methods make simplified assumptions; for example, the point spread function (PSF) is spatially consistent, the target consists of sparse point scatters, etc.
We reformulate the degradation model into a spatially variable complex-convolution model, where the near-field SAR's system response is considered.
A model-based deep learning network is designed to restore the
arXiv Detail & Related papers (2022-11-28T01:28:33Z) - AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using
Denoising Diffusion Probabilistic Models [64.24948495708337]
Atmospheric turbulence causes significant degradation to image quality by introducing blur and geometric distortion.
Various deep learning-based single image atmospheric turbulence mitigation methods, including CNN-based and GAN inversion-based, have been proposed.
Denoising Diffusion Probabilistic Models (DDPMs) have recently gained some traction because of their stable training process and their ability to generate high quality images.
arXiv Detail & Related papers (2022-08-24T03:13:04Z) - LTT-GAN: Looking Through Turbulence by Inverting GANs [86.25869403782957]
We propose the first turbulence mitigation method that makes use of visual priors encapsulated by a well-trained GAN.
Based on the visual priors, we propose to learn to preserve the identity of restored images on a periodic contextual distance.
Our method significantly outperforms prior art in both the visual quality and face verification accuracy of restored results.
arXiv Detail & Related papers (2021-12-04T16:42:13Z) - Phase Retrieval with Holography and Untrained Priors: Tackling the
Challenges of Low-Photon Nanoscale Imaging [7.984370990908576]
Phase retrieval is the inverse problem of recovering a signal from magnitude-only Fourier measurements.
We introduce a dataset-free deep learning framework for holographic phase retrieval adapted to nanoscale challenges.
arXiv Detail & Related papers (2020-12-14T10:15:07Z) - Recurrent Exposure Generation for Low-Light Face Detection [113.25331155337759]
We propose a novel Recurrent Exposure Generation (REG) module and a Multi-Exposure Detection (MED) module.
REG produces progressively and efficiently intermediate images corresponding to various exposure settings.
Such pseudo-exposures are then fused by MED to detect faces across different lighting conditions.
arXiv Detail & Related papers (2020-07-21T17:30:51Z) - Learning to Restore a Single Face Image Degraded by Atmospheric
Turbulence using CNNs [93.72048616001064]
Images captured under such condition suffer from a combination of geometric deformation and space varying blur.
We present a deep learning-based solution to the problem of restoring a turbulence-degraded face image.
arXiv Detail & Related papers (2020-07-16T15:25:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.