Karen Egiazarian

Tampere University | UTA · Department of Signal Processing

This paper addresses the challenge of regularization in lens-less single-shot phase retrieval (PR) by noise suppression. Due to the unique aspects of the PR algorithm, the noise is spatially correlated with a non-stationary level and distribution, which complicates PR's reconstruction and convergence. To address this problem, We propose an algorith...

Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at...

A tendency to increase the number of acquired remote sensing images and to make their average size larger has been observed. To manage such data, compression is needed, and lossy compression is often preferable. Since lossy compression introduces distortions, this results in worse classification and object detection. Therefore, lossy compression mu...

In this paper, we propose an approach that combines wavefront encoding and convolutional neuronal network (CNN)-based decoding for quantitative phase imaging (QPI). Encoding is realized by defocusing, and decoding by CNN trained on simulated datasets. We have demonstrated that based on the proposed approach of creating the dataset, it is possible t...

The race for miniature color cameras using flat meta-optics has rapidly developed the end-to-end design framework using neural networks. Although a large body of work has shown the potential of this methodology, the reported performance is still limited due to fundamental limitations coming from meta-optics, mismatch between simulated and resultant...

Unmanned aerial vehicle (UAV) imaging is a dynamically developing field, where the effectiveness of imaging applications highly depends on quality of the acquired images. No-reference image quality assessment is widely used for quality control and image processing management. However, there is a lack of accuracy and adequacy of existing quality met...

We propose a hyperspectral broadband phase retrieval technique with spectrally varying object and modulation phase masks. The technique is based on a complex domain version of the alternating direction method of multipliers (ADMM) and the Spectral Proximity Operators derived for Gaussian and Poisson intensity observations, which are considered as a...

The hyperspectral broadband phase retrieval is developed for a scenario where both object and modulation phase masks are spectrally varying. The proposed iterative algorithm is based on a complex domain version of the alternating direction method of multipliers (ADMM) and the novel Spectral Proximity Operators derived for Gaussian and Poissonian mu...

A problem of no-reference image visual quality assessment when images are corrupted by noise is considered in this paper. A specialized image set is proposed for the following two tasks: automatic verification of sensitivity of no-reference image visual quality metrics to noise, and analysis of blind noise level estimation methods. As a result, a m...

Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless data acquired in the form of its diffraction patterns. These patterns are acquired through a system with a coherent light source that employs a diffractive optical element (DOE) to modulate the scene, resulting in coded diffraction patterns (CDPs) at the se...

Rapid developments in synthetic aperture (SA) systems, which generate a larger aperture with greater angular resolution than is inherently possible from the physical dimensions of a single sensor alone, are leading to novel research avenues in several signal processing applications. The SAs may either use a mechanical positioner to move an antenna...

In the considered hybrid diffractive imaging system, a refractive lens is arranged simultaneously with a multilevel phase mask (MPM) as a diffractive optical element (DOE). Extended depth-of-field (EDoF) imaging and low chromatic aberrations are the two potential advantages of the proposed hybrid setup. To make use of these advantages, this paper p...

The present study addresses the issue of automatic analysis and noise reduction in dental X-ray images obtained through the Morita system. These images are characterized by spatially correlated noise with an unknown spectrum and varying standard deviation across different regions of the image. To address this issue, we propose the utilization of tw...

The hyperspectral broadband phase retrieval is developed for a scenario where both object and modulation phase masks are spectrally varying. The proposed iterative algorithm is based on a complex domain version of the alternating direction method of multipliers (ADMM) and the novel Spectral Proximity Operators derived for Gaussian and Poissonian mu...

Unmanned aerial vehicle (UAV) imaging is a dynamically developing field, where theeffectiveness of imaging applications highly depends on quality of the acquired images. No-reference image quality assessment is widely used for quality control and image processingmanagement. However, there is a lack of accuracy and adequacy of existing quality metri...

In this paper, we adopt a complex-domain cube filter (CCF) developed for hyperspectral 3D complex domain images for noise suppression of 3D complex-valued data in optical diffraction tomography. CCF is based on two processing steps: singular value decomposition (SVD) and complex-domain sparsity-based filter (CDID). SVD provides data compression and...

This Editorial paper shortly describes the papers in the Special Issue.

This is the presentation made at Synthetic Aperture Standards Committee mini-workshop held October, 19, 2022. The report relates to support of filter parameter selection based on filter efficiency prediction with particular application to Sentinel-1 SAR images and, in general, other SAR images (after necessary re-training of the perceptron NN).

End-to-end optimization of diffractive optical elements (DOEs) profile through a digital differentiable model combined with computational imaging have gained an increasing attention in emerging applications due to the compactness of resultant physical setups. Despite recent works have shown the potential of this methodology to design optics, its pe...

A fast and shallow convolutional neural network is proposed for hyperspectral image super-resolution inspired by Spatial-Spectral Reconstruction Network (SSR-NET). The feature extraction ability is improved compared to SSR-NET and other state-of-the-art methods, while the proposed network is also shallow. Numerical experiments show both the visual...

This chapter deals with lossy compression of images that have been corrupted by additive noise. The chapter's key contribution is that the analysis is done from the perspective of compressed image visual quality. Several coders are explored for which the compression ratio is regulated in various ways. Lossless coding usually does not produce suffic...

Rapid developments in synthetic aperture (SA) systems, which generate a larger aperture with greater angular resolution than is inherently possible from the physical dimensions of a single sensor alone, are leading to novel research avenues in several signal processing applications. The SAs may either use a mechanical positioner to move an antenna...

Image demosaicing is problem of interpolating full- resolution color images from raw sensor (color filter array) data. During last decade, deep neural networks have been widely used in image restoration, and in particular, in demosaicing, attaining significant performance improvement. In recent years, vision transformers have been designed and succ...

In this paper, we adopt a complex-domain cube filter (CCF) developed for hyperspectral 3D complex domain images for noise suppression of 3D complex-valued data in optical diffraction tomography. CCF is based on two processing steps: singular value decomposition (SVD) and complex-domain sparsity-based filter (CDID). SVD provides data compression and...

A hybrid imaging system is a simultaneous physical arrangement of a refractive lens and a multilevel phase mask (MPM) as a diffractive optical element (DOE). The favorable properties of the hybrid setup are improved extended-depth-of-field (EDoF) imaging and low chromatic aberrations. We built a fully differentiable image formation model in order t...

End-to-end optimization of diffractive optical elements (DOEs) profile through a digital differentiable model combined with computational imaging have gained an increasing attention in emerging applications due to the compactness of resultant physical setups. Despite recent works have shown the potential of this methodology to design optics, its pe...

Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless data acquired in the form of its diffraction patterns. These patterns are acquired through a system with a coherent light source that employs a diffractive optical element (DOE) to modulate the scene resulting in coded diffraction patterns at the sensor. Re...

We propose a novel approach for lensless single-shot phase retrieval, which provides pixel super-resolution phase imaging. The approach is based on a computational separation of carrying and object wavefronts. The imaging task is to reconstruct the object wavefront, while the carrying wavefront corrects the discrepancies between the computational m...

A power-balanced hybrid optical imaging system has a diffractive computational camera, introduced in this paper, with image formation by a refractive lens and multilevel phase mask (MPM). This system provides a long focal depth with low chromatic aberrations thanks to MPM and a high energy light concentration due to the refractive lens. We introduc...

A problem of image denoising when images are corrupted by a non-stationary noise is considered in this paper. Since in practice no a priori information on noise is available, noise statistics should be pre-estimated for image denoising. In this paper, deep convolutional neural network (CNN) based method for estimation of a map of local, patch-wise,...

We propose a novel approach and algorithm based on two preliminary tests of the optical system elements to enhance the super-resolved complex-valued imaging. The approach is developed for inverse phase imaging in a single-shot lensless optical setup. Imaging is based on wavefront modulation by a single binary phase mask. The preliminary tests compe...

The paper is devoted to the task of estimation of the parameters of spatially correlated noise and noise suppression in images. Several schemes of noise removal, including multiscale ones, are considered. A convolutional neural network (CNN) for blind estimation of the spectrum of spatially correlated noise images is proposed. It is shown that the...

The paper describes a new image database HTID for verification and training of no-reference image visual quality metrics. The database contains 2880 color images of size 1536x1024 pixels cropped from the real-life photos produced by the mobile phone cameras with various shooting and post-processing settings. Mean opinion scores for images of the da...

Algorithm enhancements are proposed for single-shot phase retrieval to reduce loss from ill-conditioning. The approach is based on prior observations, improved diffraction pattern up-sampling, and updated filters. High-quality super-resolved reconstructions are demonstrated in simulation.

Multichannel (multispectral, hyperspectral) remote sensing data may include junk component images which have quality considerably worse than other components. This can be due to intensive noise or low dynamic range of information component in such junk channels (sub-bands). Such component images are sometimes ignored (not used in further processing...

A novel formulation of the hyperspectral broadband phase retrieval is developed for the scenario where both object and modulation phase masks are spectrally varying. The proposed algorithm is based on a complex domain version of the alternating direction method of multipliers (ADMM) and Spectral Proximity Operators (SPO) derived for Gaussian and Po...

Radar imaging has many advantages. Meanwhile, SAR images suffer from a noise-like phenomenon called speckle. Many despeckling methods have been proposed to date but there is still no common opinion as to what the best filter is and/or what are its parameters (window or block size, thresholds, etc.). The local statistic Lee filter is one of the most...

A similarity search in images has become a typical operation in many applications. A presence of noise in images greatly affects the correctness of detection of similar image blocks, resulting in a reduction of efficiency of image processing methods, e.g., non-local denoising. In this paper, we study noise immunity of various distance measures (sim...

Image denoising is a classical preprocessing stage used to enhance images. However, it is well known that there are many practical cases where different image denoising methods produce images with inappropriate visual quality, which makes an application of image denoising useless. Because of this, it is desirable to detect such cases in advance and...

The power-balanced hybrid optical imaging system is a special design of a computational camera, introduced in this paper, with image formation by a refractive lens and Multilevel Phase Mask (MPM) as a diffractive optical element (DoE). This system provides a long focal depth and low chromatic aberrations thanks to MPM, and a high energy light conce...

In this paper, we employ a deep convolutional neural network for the solution of the phase retrieval problem in a lensless optical system from a single observation. We utilize U-net-like structured DCNN to reconstruct phase from the amplitude images at the sensor plane, and after applying computational backpropagation, the complex objects’ amplitud...

Optimal optical transfer function (OTF) is proposed for RGB inverse imaging with extended depth of field (DoF) and diminished color aberrations. This optimal OTF is derived as the Wiener filter of broadband defocused OTFs. The performance of this new inverse imaging is demonstrated for optical setups with lens and lensless with a multilevel phase m...

A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large c...

https://sigport.org/documents/broadband-hyperspectral-phase-retrieval-noisy-data Hyperspectral (HS) imaging retrieves information from data obtained across a wide spectral range of spectral channels. The object to reconstruct is a 3D cube, where two coordinates are spatial and the third one is spectral. We assume that this cube is complex-valued,...

Hyperspectral (HS) imaging retrieves information from data obtained across a wide spectral range of spectral channels. The object to reconstruct is a 3D cube, where two coordinates are spatial and third one is spectral. We assume that this cube is complex-valued, i.e. characterized spatially-frequency varying amplitude and phase. The observations a...

This paper deals with processing of multichannel remote sensing images for which it is difficult and often impossible to carry out any stage of data processing in an interactive manner and, thus, full or, at least, partial automation is needed. Despite of existence of many methods, it is difficult to choose the best or an appropriate one and/or to...

Traditional approach to collect mean opinion score (MOS) values for evaluation of full-reference image quality metrics has two serious drawbacks. The first drawback is a nonlinearity of MOS, only partially compensated by the use of rank order correlation coefficients in a further analysis. The second drawback are limitations on number of distortion...

Remote sensing images are subject to different types of degradations. The visual quality of such images is important because their visual inspection and analysis are still widely used in practice. To characterize the visual quality of remote sensing images, the use of specialized visual quality metrics is desired. Although the attempts to create su...

A novel phase retrieval algorithm for broadband hyperspectral phase imaging from noisy intensity observations is proposed. It utilizes advantages of the Fourier transform spectroscopy in the self-referencing optical setup and provides additional, beyond spectral intensity distribution, reconstruction of the investigated object’s phase. The noise am...

To enhance images, often one has to apply filtering (denoising). However, there are several open issues within denoising. One of them is that sometimes denoising can be not efficient. Another issue regards a selection of an appropriate filter and setting its parameters. As a particular case, we consider 2D DCT-based filter with 8x8 pixel fully over...

We consider the problem of compressive sensed video recovery via iterative thresholding algorithm. Traditionally, it is assumed that some fixed sparsifying transform is applied at each iteration of the algorithm. In order to improve the recovery performance, at each iteration the thresholding could be applied for different transforms in order to obt...

A novel algorithm for reconstruction of hyperspectral 3D complex domain images (phase/amplitude) from noisy complex domain observations has been developed and studied. This algorithm starts from the SVD (singular value decomposition) analysis of the observed complex-valued data and looks for the optimal low dimension eigenspace. These eigenspace im...

This paper proposes a learning-based denoising method called FlashLight CNN (FLCNN) that implements a deep neural network for image denoising. The proposed approach is based on deep residual networks and inception networks and it is able to leverage many more parameters than residual networks alone for denoising grayscale images corrupted by additi...

A novel phase retrieval algorithm for broadband hyperspectral noisy data is proposed. It utilizes the advantages of the Fourier Transform spectroscopy in a self-referencing optical setup and additionally beyond the spectral information provides a reconstruction of the investigated object's phase. The noise multiplicative Fellgett's disadvantage is...

Design and optimization of lensless phase-retrieval optical system with phase modulation of free-space propagation wavefront is proposed for subpixel imaging to achieve super-resolution reconstruction. Contrary to the traditional super-resolution phase-retrieval, the method in this paper requires a single observation only and uses the advanced Supe...

Synthetic aperture radar (SAR) images are corrupted by a specific noise-like phenomenon called speckle that prevents efficient processing of remote sensing data. There are many denoising methods already proposed including well known (local statistic) Lee filter. Its performance in terms of different criteria depends on several factors including ima...

We consider hyperspectral phase/amplitude imaging from hyperspectral complex-valued noisy observations. Block-matching and grouping of similar patches are main instruments of the proposed algorithms. The search neighborhood for similar patches spans both the spectral and 2D spatial dimensions. SVD analysis of 3D grouped patches is used for design o...

A digital holographic approach is used for calibrating the phase modulation in every pixel of the spatial light modulator (SLM). The phase reconstruction method involves a variational algorithm based on the local least square technique.

The report discusses hyperspectral interferometric measurement systems for the tasks of wavefront sensing and amplitude-phase objects reconstruction. The influence of spatial and spectral phase in the interferometer arms are analyzed in numerical simulation.