Image degradation characteristics and restoration based on regularization for diffractive imaging

Diffractive membrane imaging systems have been an important development trend for high-orbit satellite cameras owing to their advantages of large aperture, light weight, rapid manufacture, and low cost. However, caused by the cross-coupling effects of diffraction imaging, membrane properties, subaperture stitching, on-orbit disturbances, and other physical factors, lager-aperture space diffractive membrane imaging systems have specific and complex degradation characteristics: the modulation transfer function (MTF) and signal-to-noise ratio (SNR) have more prominent degradation and serious space-variant characteristics over fields of view, with obvious background radiation properties that seriously affect the application of imaging products. To address this problem, this study established a global information transmission model by characterizing the PSF and background radiation in a full field of view to represent the imaging law of an on-orbit system. Aiming at the inverse problem of the information transmission model, we also propose a novel image inversion restoration method for the special degradation characteristics. In particular, the effect of diffraction efficiency is introduced into the inversion restoration method to solve the background radiation problem. Moreover, we innovatively designed matrix regularization parameters to further improve the correction ability of spatial variation. When the diffraction efficiency was experimentally higher than 60% and the mean measured spatial variability was less than 0.2, the proposed method exhibited a satisfactory processing performance, and could improve multiobjective comprehensive processing, such as transfer function compensation, spatial variation correction, and background radiation removal.

X-ray transmission imaging has been used in a variety of applications for high-resolution measurements based on shape and density. Similarly, X-ray diffraction (XRD) imaging has been used widely for molecular structure-based identification of materials. Combining these X-ray methods has the potential to provide high-resolution material identification, exceeding the capabilities of either modality alone. However, XRD imaging methods have been limited in application by their long measurement times and poor spatial resolution, which has generally precluded combined, rapid measurements of X-ray transmission and diffraction. In this work, we present a novel X-ray fan beam coded aperture transmission and diffraction imaging system, developed using commercially available components, for rapid and accurate non-destructive imaging of industrial and biomedical specimens. The imaging system uses a 160 kV Bremsstrahlung X-ray source while achieving a spatial resolution of ≈ 1 × 1 mm2 and a spectral accuracy of > 95% with only 15 s exposures per 150 mm fan beam slice. Applications of this technology are reported in geological imaging, pharmaceutical inspection, and medical diagnosis. The performance of the imaging system indicates improved material differentiation relative to transmission imaging alone at scan times suitable for a variety of industrial and biomedical applications.

In order to verify the technology of the membrane diffractive imaging system for Chinese next generation geo-stationary earth orbit (GEO) satellite, a series of ground experiments have been carried out using a membrane optical camera with 80 mm aperture (Φ80) lens. The inherent chromatic aberration due to diffractive imaging appears in the obtained data. To address the issue, an effective color restoration algorithm framework by matching, tailoring, and non-linearly stretching the image histograms is proposed in this letter. Experimental results show the proposed approach has good performances in color restoration of the diffractive optical images than previous methods. The effectiveness and robustness of the algorithm are also quantitatively assessed using various color deviation indexes. The results indicate that the chromatic aberration of diffractive images can be effectively removed by about 85%. Also, the proposed method presents reasonable computational efficiency.

To achieve the demand of earth observation with high spatiotemporal resolution and large scale, a novel optical imaging system is planning to equip on Chinese next generation geo-stationary earth orbit (GEO) satellite. The primary mirror of camera is made of a set of thin films and adopts the diffraction imaging mechanism. A series of ground experiments have been carried out using a thin-film camera with 80 mm aperture for the technical verification. The inherent chromatic aberration due to diffraction imaging appears in the obtained data. To address the issue, a color restoration algorithm by matching, tailoring and linearly stretching the histograms is proposed in this paper. Experimental results show the proposed approach has good performances in color restoration of the diffractive optical images. The effectiveness and robustness of the algorithm are also assessed with various color deviation indexes.

Color correction with 3D lookup tables in diffractive optical imaging systems

Diffraction imaging spectrometer cannot acquire imaging spectral data without calculation and inversion. In this paper, the imaging process of the diffraction imaging spectrometer and the principle of the data error from both space and spectra are analyzed. To solve the problems of low definition of the reconstruction and the ringing in it occurring under the condition that the point spread function (PSF) is larger, a new algorithm is proposed based on improved inverse Wiener filtering. The improved method regards the reconstruction result of Wiener filtering as a new fuzzy image, and recalculates the PSF of the new fuzzy image based on the analysis of the diffraction characteristics and error. Inverse iterative Wiener filtering is used to improve the definition of the reconstruction, and then the noise needs to be removed according to the distribution of the spatial and spectral features. Simulated diffraction imaging spectral data are used to verify the correctness of the algorithm proposed in this paper. A reconstruction without ringing can be obtained when the standard deviation of PSF is 2.5, and both of the definition and detail ability are higher than those of the traditional reconstruction. The reconstruction using the improved algorithm proposed in this paper can satisfy the applications of the diffraction imaging spectral data.

Satellites have many high-, medium-, and low-frequency micro vibration sources that lead to the optical axis jitter of the optical load and subsequently degrade the remote sensing image quality. To address this problem, this paper developed an image motion detection and restoration method based on an inertial reference laser, and describe edits principle and key components. To verify the feasibility and performance of this method, this paper also built an image motion measurement and restoration system based on an inertial reference laser, which comprised a camera (including the inertial reference laser unit and a Hartmann wavefront sensor), an integrating sphere, a simulated image target, a parallel light pope, a vibration isolation platform, a vibration generator, and a 6 degrees of freedom platform. The image restoration principle was also described. The background noise in the experiment environment was measured, and an image motion measurement accuracy experiment was performed. Verification experiments of image restoration were also conducted under various working conditions. The experiment results showed that the error of image motion detection based on the inertial reference laser was less than 0.12 pixels (root mean square). By using image motion data to improve image quality, the modulation transfer function (MTF) of the restored image was increased to 1.61–1.88 times that of the original image MTF. The image motion data could be used as feedback to the fast steering mirror to compensate for the satellite jitter in real time and to directly obtain high-quality images.

This paper introduces a novel lensless full colour diffractive computational imaging system with a planar Multilevel Phase Mask (MPM) as a diffractive optical element (DOE). The novelty concerns: a methodology of MPM design for improved depth of focus (DoF); design of PSFs for RGB imaging and an inverse imaging algorithm with sparse colour image modelling simultaneous for all RGB channels. MPMs are step-wise invariant. The cubic wavefront coding (WFC) is incorporated in MPMs with optimization of number of levels and width of invariant steps. This design of MPM makes the system robust with respect to defocus (improves DoF) and diminish chromatic aberrations typical for DOEs. Broadband multichannel test-images are exploited for design and testing of the lensless system. We consider two alternative optical setups: Wavelength Multiplexing (WM) and Wavelength Division (WD). In WM, the light beam is broadband multichannel with light sources radiating all wavelengths simultaneously and a CMOS sensor is equipped with a Bayer colour filter array (CFA) for registration of spectral measurements. In this setup, a single MPM is designed for the broadband multichannel light beams. In WD, separate exposures of RGB channels are registered by a broadband grey-scale CCD sensor. Different MPMs are designed for each of the RGB channels. Simulation experiments demonstrate the essentially extended DoF of the designed lensless systems and the advanced accuracy and quality of imaging with respect to the corresponding WM and WD systems with refractive lenses. Due to robustness of the designed lensless system to chromatic aberrations, this advantage has a place even with respect to the lens-system.

Coded Aperture Snapshot Spectral Imaging (CASSI) is an effective tool to capture spectral images, which has the advantages of snapshot imaging, high luminous flux, high signal-to-noise ratio and low sampling frequency. However, conventional CASSI generally uses refractive prisms or gratings for spectral dispersion, which leads to the nonlinear dispersion phenomenon and the requirement of large detector chip respectively. To overcome these issues, conventional refractive prisms or gratings are replaced by an axially dispersive diffractive optical element (DOE, i.e., computational optics) together with a RGB Bayer filter (i.e., a color-coded aperture) in this study. Specifically, the spatial-spectral information of a test scene is jointly modulated by the DOE and the Bayer filter integrated with a sensor chip. A fully differentiable imaging model is built based on the principle of diffractive optics and the deep learning technology. Furthermore, an optimization design of the DOE with the coded aperture is realized through an end-to-end approach, the output spectral images of which are restored by a Res-Unet neural network. Several simulation results show that up to 31 high-fidelity spectral bands in the range of 400 to 700 nm with a good spatial and spectral resolution can be recovered by the proposed snapshot system.

Recent works that utilized deep models have achieved superior results in various image restoration (IR) applications. Such approach is typically supervised, which requires a corpus of training images with distributions similar to the images to be recovered. On the other hand, the shallow methods, which are usually unsupervised remain promising performance in many inverse problems, e.g., image deblurring and image compressive sensing (CS), as they can effectively leverage nonlocal self-similarity priors of natural images. However, most of such methods are patch-based leading to the restored images with various artifacts due to naive patch aggregation in addition to the slow speed. Using either approach alone usually limits performance and generalizability in IR tasks. In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely, internal and external, shallow and deep, and non-local and local priors. We then propose a novel hybrid plug-and-play (H-PnP) framework based on the LRD model for IR. Following this, a simple yet effective algorithm is developed to solve the proposed H-PnP based IR problems. Extensive experimental results on several representative IR tasks, including image deblurring, image CS and image deblocking, demonstrate that the proposed H-PnP algorithm achieves favorable performance compared to many popular or state-of-the-art IR methods in terms of both objective and visual perception.

Due to the wave nature of diffraction, purely diffractive systems have been plagued by large chromatic and off-axis aberrations. Accordingly, there has not been much work in purely diffractive wideband imaging systems. In this paper, we present a method for the design and subsequent optimization of diffractive doublets for wideband, on-axis imaging. The method can most easily be explained with reference to Kato’s recent work[1], although the present work was arrived at independently.

A thorough and complete assessment of microstructure of materials requires a wide variety of analytical techniques, which should be sensitive to at all length scales - from mms to atomic scale. While there have been rapid advances in imaging and spectroscopy techniques for microstructural analysis - at all length scales, techniques for crystallographic analysis of microstructure have primarily relied on bulk x-ray/neutron diffraction and TEM. X-ray and neutron diffraction techniques, though very powerful, are primarily bulk techniques and extraction of local crystallography is formidable if not impossible. On the other hand, TEM diffraction techniques provide precise crystallographic information, but at a much smaller length-scale and suffer from poor statistics and tedious specimen preparation procedures. With the advent of commercially available electron backscattered diffraction (EBSD) and orientational imaging (OIM) systems for SEM, and sophisticated pattern recognition procedures, it is now possible to bridge the length-scale gap between bulk and TEM diffraction techniques.

This paper presents a novel variational approach to imposing statistical constraints on the output of both image generation (typically to perform texture synthesis) and image restoration (for instance, to achieve denoising and inpainting) methods. The empirical distributions of linear or nonlinear image descriptors are imposed to be close to some input distributions by minimizing a Wasserstein loss, i.e., the optimal transport distance between the distributions. We advocate the use of a Wasserstein distance because it is robust when using discrete distributions without the need to resort to kernel estimators. We showcase the use of different descriptors to tackle various image processing applications. These descriptors include linear wavelet-based filtering to account for simple textures, nonlinear sparse coding coefficients for more complicated patterns, and the image gradient to restore sharper contents. For applications to texture synthesis, the input distributions are the empirical distributions compu...

Eyeglass is a very large aperture (25 - 100 meter) space telescope consisting of two distinct spacecraft, separated in space by several kilometers. A diffractive lens provides the telescope's large aperture, and a separate, much smaller, space telescope serves as its mobile eyepiece. Use of a transmissive diffractive lens solves two basic problems associated with very large aperture space telescopes; it is inherently fieldable (lightweight and flat, hence packagable and deployable) and virtually eliminates the traditional, very tight, surface shape tolerances faced by reflecting apertures. The potential drawback to use of a diffractive primary (very narrow spectral bandwidth) is eliminated by corrective optics in the telescope's eyepiece. The Eyeglass can provide diffraction-limited imaging with either single-band, multiband, or continuous spectral coverage. Broadband diffractive telescopes have been built at LLNL and have demonstrated diffraction-limited performance over a 40% spectral bandwidth (0.48 - 0.72 μm). As one approach to package a large aperture for launch, a foldable lens has been built and demonstrated. A 75 cm aperture diffractive lens was constructed from 6 panels of 1 mm thick silica; it achieved diffraction-limited performance both before and after folding. This multiple panel, folding lens, approach is currently being scaled-up at LLNL. We are building a 5 meter aperture foldable lens, involving 72 panels of 700 μm thick glass sheets, diffractively patterned to operate as coherent f/50 lens.

Image restoration and enhancement are ones of fundamental problems in image processing. Many research activities, therefore, have been continuously carried out during almost three decades [1, 2, 3]. Inverse filtering, Wiener filtering with or without constrains, and Kalman filtering-smoothing techniques have been applied to the image restoration problem in the cases of either known or unknown blurring functions. Many methods in image restoration and enhancement rely upon the Fourier transformation and its discrete version; DFT (discrete Fourier transformation).