Contrast Degradation Research Articles

Temporal prepulse contrast degradation in high-intensity CPA lasers from anisotropy of amplifier gain media.

We present a study of the temporal prepulse contrast degradation of high focused intensity pulses produced in CPA laser systems due to imperfections in amplifier design, alignment of amplifier components, and crystal inhomogeneity. Using an extended cross-polarized imaging technique, we demonstrate the presence of multiple crystal domains inside Ti:sapphire slabs with ≈10cm diameter. The results of our numerical calculations show that crystalline c-axis orientation inhomogeneity caused by these crystal domains can lead to the generation of prepulses with a relative contrast of >10-10 within several picoseconds before the main pulse. In a multiple-slab amplifier head configuration sometimes used in high-repetition-rate systems, the misalignment of the crystalline c-axes of the amplifier slab with respect to each other can lead to the generation of prepulses with relative contrast as high as 10-6, depending on the magnitude of misalignment.

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

AbstractUnderwater image processing is a vast area of research to identify blurred objects in the ocean or sea environments. The light absorbed and scattered by the underwater medium create artifacts in the acquired images due to low‐light effects, poor visibility, darkness, illumination variation, and color contrast degradation. In this research work, a novel optical image preprocessing framework was proposed to restore the color contrast of images obtained from various water‐colored environments. Domain transform‐based multiscale image decomposition method was used to extract the base, residual, and detail layers. Then, the smoothed image was reconstructed from the base layer by applying the adaptive iterative backward‐projection algorithm. To obtain dehazed images, a multiscale fusion‐based algorithm was developed, which fused the detail layers and reconstructed the images. The outcome of the proposed framework revealed that the contrast degradation and low‐light‐related artifacts present in the acquired images from various water‐colored environments were found to be considerably reduced. The experimental results of the proposed method exhibit scalable improvements over the other methods in terms of visual perception and estimated performance metrics (underwater image quality measure = 6.1186; peak signal‐to‐noise ratio = 26.68; feature points matching = 96 points; semantic image segmentation accuracy = 0.8465 and processing time of 0.11 seconds on a single image). The novelty of this method relies on the fusion of well‐established techniques, which outperformed the advanced techniques like deep learning method in terms of simplicity, speed, performance, datasets, image resolution, and applications.

Image Dehazing and Enhancement: A Review

The enhancement of images is an image processing method that highlights certain image information according to specific needs and, at the same time, weakens or removes unwanted information. In the field of computer and machine vision, haziness and fog lead to degradation of images using different degradation mechanisms, including contrast attenuation, blurring, and degradation of the pixels. This limits machine vision systems efficiency such as video monitoring, target tracking, and recognition. Different dark channel single image dehazing algorithms have been designed quickly and efficiently to address image hazing problems. These algorithms rely on the dark channel theory to estimate the atmospheric light which is a crucial dehazing parameter. In this paper, a review of image dehazing and enhancement has been presented.

Full-field measurement of complex objects illuminated by an ultrashort pulse laser using delay-line sweeping off-axis interferometry.

Measuring the complex field of ultrashort pulse lasers plays a fundamental role in light wavefront manipulation and nonlinear phenomena investigation; yet, it still constitutes a challenge for both full-field and high-resolution characterization due to the short coherent length. We proposed and implemented an off-axis interference system with a delay-line sweeping technique to overcome the fringe contrast degradation caused by the envelope mismatch between interfering pulses, resulting in an increased effective analysis area. The effectiveness of the proposed method was first demonstrated by measuring a complex field generated by a phase-only spatial light modulator, where a four-pixel binning technique was adopted for both amplitude and phase modulation; then it was used for the measurement of the second harmonic generation signal of a urea crystal sample. The experimental results show that the proposed method is capable of measuring complex fields having fine features within the full field. The proposed technique can be applied for strongly scattering medium refocusing and adaptive optics, where measuring the complex field of ultrashort pulse lasers is essential.

Concave Array Ultrasonic Transducer From Multilayer Piezoelectric Ceramic for Photoacoustic Imaging.

Implementation of piezoelectric multilayer ceramic (MLC) is an effective way to reduce impedance and improve the performance of linear-array transducer for ultrasonic system applications. However, the ultrasonic image derived from a planar linear-array transducer generally suffers from degradation of lateral resolution and contrast. In this article, we designed and fabricated a focused 5-MHz 128-element linear-array ultrasonic transducer with concave structure using five-layered 0.1Pb (Ni1/3Nb2/3)O3 -0.35Pb(Zn1/3Nb2/3)O3 -0.15Pb(Mg1/3Nb2/3)O3-0.1PbZrO3-0.3PbTiO3 (PNN-PZN-PMN-PZ-PT) piezo- electric ceramic. The transducer showed a bandwidth of 63% at -6 dB and the lateral resolution up to 0.33 mm. An improved transmission signal of 90% higher than a commercial single-layer ceramic transducer was also achieved. We further demonstrated high-resolution photoacoustic imaging with the obtained concave linear-array transducer.

Under Display Camera Quad Bayer Raw Image Restoration using Deep Learning

Can a mobile camera see better through display? Under Display Camera (UDC) is the most awaited feature in mobile market in 2020 enabling more preferable user experience, however, there are technological obstacles to obtain acceptable UDC image quality. Mobile OLED panels are struggling to reach beyond 20% of light transmittance, leading to challenging capture conditions. To improve light sensitivity, some solutions use binned output losing spatial resolution. Optical diffraction of light in a panel induces contrast degradation and various visual artifacts including image ghosts, yellowish tint etc. Standard approach to address image quality issues is to improve blocks in the imaging pipeline including Image Signal Processor (ISP) and deblur block. In this work, we propose a novel approach to improve UDC image quality - we replace all blocks in UDC pipeline with all-in-one network – UDC d^Net. Proposed solution can deblur and reconstruct full resolution image directly from non-Bayer raw image, e.g. Quad Bayer, without requiring remosaic algorithm that rearranges non-Bayer to Bayer. Proposed network has a very large receptive field and can easily deal with large-scale visual artifacts including color moiré and ghosts. Experiments show significant improvement in image quality vs conventional pipeline – over 4dB in PSNR on popular benchmark - Kodak dataset.

Region-Based Dehazing via Dual-Supervised Triple-Convolutional Network

Most physical model-based dehazing methods are subject to contrast degradation in a dark or shadow region because of the mismatch between the physical model and real haze. This degradation decreases the quality of dehazed images. Furthermore, the retinex-haze combined models can cause the brightness saturation problem in a haze region. For this reason, the retinex-haze combined approaches are not appropriate to enhance the real-world haze images. To solve these problems, we present a novel region-based dehazing method via dual-supervised triple-convolutional network (TCN). More specifically, the proposed network first simulates the mismatch problem based on the region-model. Next, we then train the proposed triple-convolutional network, which can estimate the degraded regions. We then present a novel dual-supervised learning method to efficiently train the networks using a non-ideal dataset. Experimental results show that the proposed method outperforms state-of-the-art approaches in solving complex haze problems. The output of the proposed network has a high-similarity index in most cases for various benchmark dataset. Our approach also produces high-quality images in real haze image datasets.

A Trainable Monogenic ConvNet Layer Robust in Front of Large Contrast Changes in Image Classification

Convolutional Neural Networks (ConvNets) at present achieve remarkable performance in image classification tasks. However, current ConvNets cannot guarantee the capabilities of the mammalian visual systems such as invariance to contrast and illumination changes. Some ideas to overcome the illumination and contrast variations usually have to be tuned manually and tend to fail when tested with other types of data degradation. In this context, we present a new bio-inspired entry layer, M6, which detects low-level geometric features (lines, edges, and orientations) which are similar to patterns detected by the V1 visual cortex. This new trainable layer is capable of coping with image classification even with large contrast variations. The explanation for this behavior is the monogenic signal geometry, which represents each pixel value in a 3D space using quaternions, a fact that confers a degree of explainability to the networks. We compare M6 with a conventional convolutional layer (C) and a deterministic quaternion local phase layer (Q9). The experimental setup is designed to evaluate the robustness of our M6 enriched ConvNet model and includes three architectures, four datasets, three types of contrast degradation (including non-uniform haze degradations). The numerical results reveal that the models with M6 are the most robust in front of any kind of contrast variations. This amounts to a significant enhancement of the C models, which usually have reasonably good performance only when the same training and test degradation are used, except for the case of maximum degradation. Moreover, the Structural Similarity Index Measure (SSIM) is used to analyze and explain the robustness effect of the M6 feature maps under any kind of contrast degradations.

A novel design of double chirped pulse amplification laser systems for fourth-order dispersion control.

A novel design of double chirped pulse amplification laser systems implementing a combination of negatively and positively chirped pulse amplification is proposed for the first time. Without utilizing any extra dispersion compensation element, this design can sufficiently cancel out the second-, third- and especially fourth-order dispersion simultaneously, just by optimizing the parameters of the stretcher and compressor in first chirped pulse amplification stage which applies negatively chirped pulse amplification. The numerical results indicate that near Fourier-transform-limited pulse duration about 20fs can be achieved in high-peak-power femtosecond laser systems up to multi-Petawatt level. This design not only provides a feasible solution for the dispersion control in high-contrast and high-peak-power femtosecond laser systems, but also can avoid the degradation of temporal contrast induced by seed energy loss in the presence of additional dispersion compensation components.

A Scale-Adaptive Matching Algorithm for Underwater Acoustic and Optical Images.

Underwater acoustic and optical data fusion has been developed in recent decades. Matching of underwater acoustic and optical images is a fundamental and critical problem in underwater exploration because it usually acts as the key step in many applications, such as target detection, ocean observation, and joint positioning. In this study, a method of matching the same underwater object in acoustic and optical images was designed, consisting of two steps. First, an enhancement step is used to enhance the images and ensure the accuracy of the matching results based on iterative processing and estimate similarity. The acoustic and optical images are first pre-processed with the aim of eliminating the influence of contrast degradation, contour blur, and image noise. A method for image enhancement was designed based on iterative processing. In addition, a new similarity estimation method for acoustic and optical images is also proposed to provide the enhancement effect. Second, a matching step is used to accurately find the corresponding object in the acoustic images that appears in the underwater optical images. In the matching process, a correlation filter is applied to determine the correlation for matching between images. Due to the differences of angle and imaging principle between underwater optical and acoustic images, there may be major differences of size between two images of the same object. In order to eliminate the effect of these differences, we introduce the Gaussian scale-space, which is fused with multi-scale detection to determine the matching results. Therefore, the algorithm is insensitive to scale differences. Extensive experiments demonstrate the effectiveness and accuracy of our proposed method in matching acoustic and optical images.

Multi-scale enhancement fusion for underwater sea cucumber images based on human visual system modelling

The vision computing techniques are widely used in the marine industry. In order to improve the detection and recognition accuracy of artificial intelligence-robotics, we propose a novel underwater image enhancement algorithm, using a multi-scale fusion approach based on the properties of the human visual system. Our method fuses the results of underwater image enhancement algorithms that deal with the color-casting, sharpness and contrast degradation. The method is further weighted by a human visual system-based image structure map that combines Michaelson-like contrast map, saliency map, dark channel map and exposed map. The multi-scale fusion strategy is used to avoid the artifacts of sharpness blending based on Laplacian image representation. The results show that our algorithm can recover more detail information of dark regions and improve the overall visual quality of underwater images.

Color image dehazing using gradient channel prior and guided L0 filter

Images obtained in poor weather circumstances such as fog, haze, smog, and thin cloud, suffer from severe contrast, texture, edge, and color degradation issues. To restore these weather degraded images, haze removal techniques are required. An efficient Gradient channel prior (GCP) is designed in this paper. It overcomes various issues such as texture distortion, transmission map misestimation, color distortion, and edge degradation. Thereafter, the transmission map is further refined using a guided L0 filter. Finally, the restoration model is also improved to reduce the over-saturation of pixels problem associated with the existing haze removal techniques. Extensive experimental results demonstrate that the proposed technique can significantly restore the hazy images, even if images contain high density of haze.

Stable and scalable smart window based on polymer stabilized liquid crystals

ABSTRACTA stable and scalable polymer‐stabilized liquid crystal window which electrically switches from transparent to opaque has been fabricated. Scanning electron microscope measurement shows that higher polymer concentration will induce denser polymer network in polymer stabilized liquid crystal system and then stronger anchoring force between polymer network and liquid crystal molecules, which resulting in larger operating voltage. The cell with larger cell gap has a lower saturated transmittance in the voltage‐on state, which attributed to a larger number of scattering domains in thick cell. The optimized cell exhibits a highly transparent voltage‐off state (3.5% haze) and a voltage‐on scattering state (98% haze) with the threshold voltage of around 20 V. The durability test shows that the optical device switches at least 100,000 times without degradation of optical contrast and shows a high temperature tolerance. Meanwhile, a 40 × 50 cm2 window has been developed in an industrial production line showing the same optical properties. Our results demonstrate the fabrication of smart windows with a highly transparent rest state and high optical contrast on a commercial mass production scale, making them attractive for applications in buildings, automobiles, and switchable sunglasses for light management and potentially energy saving. © 2020 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48917.

Shape prior metal artefact reduction algorithm for industrial 3D cone beam CT

ABSTRACT Metal artefact reduction has become a challenging issue for practical X-ray CT applications since metal artefacts severely cause contrast degradation and the misinterpretation of information about the property and structure of a scanned object. In this paper, we propose a methodology to reduce metal artefacts in a three-dimensional industrial cone beam CT system. We develop a registration technique managing the three dimensional data in order to find accurate segmentation regions needed for the proposed algorithm. Through various simulations and experiments, we verify that the proposed algorithm reduces metal artefacts successfully.

Improving the Performance of Image Fusion Based on Visual Saliency Weight Map Combined With CNN

Convolutional neural networks (CNN) with their deep feature extraction capability have recently been applied in numerous image fusion tasks. However, the image fusion of infrared and visible images leads to loss of fine details and degradation of contrast in the fused image. This deterioration in the image is associated with the conventional “averaging” rule for base layer fusion and relatively large feature extraction by CNN. To overcome these problems, an effective fusion framework based on visual saliency weight map (VSWM) combined with CNN is proposed. The proposed framework first employs VSWM method to improve the contrast of an image under consideration. Next, the fine details in the image are preserved by applying multi-resolution singular value decomposition (MSVD) before further processing by CNN. The promising experimental results show that the proposed method outperforms state-of-the-art methods by scoring the highest over different evaluation metrics such as Q0, multiscale structural similarity (MS_SSIM), and the sum of correlations of differences (SCD).

Blue Channel and Fusion for Sandstorm Image Enhancement

Due to the scattering and absorption of light by sandstorm particles, images taken in sandstorm environments have low contrast, color distortion and other degradation problems, which seriously affect outdoor computer vision systems. In this paper, we propose a novel method to enhance images captured in sandstorm weather conditions. First, the method builds on the blending of two images that are directly derived from the original degraded image. Second, we use multilayer decomposition technology to enhance image details and use a blue channel and white balancing technology to restore image contrast and chromaticity. Third, we associate weight maps to improve image edge contrast. Finally, the Laplacian pyramid fusion method is used to obtain the fusion results of the sandstorm-free color correction image. The experimental results demonstrate that the proposed method can effectively restore the fade characteristics of sandstorm-degraded images and improve the clarity of the images. Experimental results via subjective and objective evaluations demonstrate that the proposed method can significantly improve images captured during sandstorm weather conditions, and the results are better than other methods.

Simulation and patient studies of scatter correction in cardiac SPECT imaging

Introduction: Myocardial perfusion imaging is a nuclear medicine imaging method that is used to detect coronary artery diseases. One of the main sources of error in this imaging method is the detection of Compton scattered photons in the photopeak energy window used for data acquisition. This results in the degradation of the image contrast, and therefore decreases the diagnostic accuracy. Material and Methods: In this study, the efficiency of dual-energy window (DEW) correction method regarding the reduction of the undesirable influence of scattering was investigated using the images acquired from the 3D-NCAT simulated phantom, and a group of patients (18 males and 27 females) in both rest and stressful situations. To evaluate the scatter correction method, the image contrasts are calculated before and after applying the scatter correction. Results: The results obtained from this study indicated that the calculated image contrasts enhanced by applying the scatter correction in both simulation and patient studies. In the simulation study, the relative values of increase in image contrast are about 10.15% and 12.58% when using a k value equal to 0.5, and the linear fitting method, respectively. In the patient study, the relative values of increase in image contrasts regarding the rest and stress situations were about 13.63% and 10.84% for females and 12.03% and 10.56% for males, respectively. Conclusion: The utilization of the DEW method for scatter correction of cardiac SPECT images results in an increase in the image contrast and the improvement of the image quality.

Resolution enhancement in magnetic particle imaging via phase-weighting

Spatial resolution is a key metric for characterizing any imaging modality. Here we describe magnetic particle imaging (MPI) experiments and complementary magnetic particle spectroscopy (MPS) experiments that reveal and probe significant variations in MPI spatial resolution that are intrinsically coupled to the magnetization dynamics of superparamagnetic nanoparticles. In particular, employing a phase-sensitive MPI detection scheme, we observe a marked spatial sharpening effect as the phase of the demodulation reference is advanced. This increase in spatial resolution is accompanied by degradation of image contrast, to the point where reconstructed images effectively vanish when the demodulation phase is advanced by approximately 90 degrees relative to the phase that yields images with the highest signal-to-noise ratio (SNR). Relaxation- or phase-weighted magnetic particle images with apparent spatial resolutions of order a few hundred microns or better, and with SNRs of order 10 dB or more, are presented, and a qualitative framework for interpreting the role of phase as it pertains to spatial resolution in MPI is introduced.

Fast Dual-Stimuli-Responsive Dynamic Surface Wrinkles with High Bistability for Smart Windows and Rewritable Optical Displays

The dynamic dual-stimuli-responsive surface wrinkles on a bilayer film with high bistability are unattainable and attractive for the applications of smart windows and optical displays. Here, we report a new strategy in developing moisture and temperature dual-responsive surface wrinkles on the polyvinyl alcohol/polydimethylsiloxane (PVA/PDMS) bilayer film by rationally designing the modulus changes of the PVA skin layer upon moisture and temperature. By optimizing the thickness of the PVA layer to 4.5 μm, the as-prepared surface wrinkles show long-awaited properties, such as fast response time, excellent reversibility without degradation of optical contrast, and high light transmittance modulation, which greatly outperforms the reported surface wrinkles. Moreover, the surface wrinkles on the bilayer film remain highly bistable without additional energy consumption for more than five months in ambient room conditions both in the opaque and transparent states. These promising dual-stimuli-responsive surface wrinkles on bilayer films hold great promises for various applications triggered by moisture and temperature, such as smart windows and rewritable optical displays.

Fast Fourier-Domain Optimization Using Hybrid L1−/L${}_{p}$ -Norm for Autofocus in Airborne SAR Imaging

In maneuverable airborne synthetic aperture radar (SAR), high-frequency phase errors (HPEs), which are hard to be compensated by the phase gradient autofocus, often occur causing spurious targets and contrast degradation in SAR imagery. The metric-optimization approaches improve the phase error estimation scalability, including HPEs, but show much lower speed because of the computational burden by the inverse fast Fourier transform (IFFT). Proposed autofocus based on the metric optimization enables the rapid estimation with the scalability including the HPE by eliminating the IFFT. In addition, the metric is adapted to the range compressed data corresponding to the input of the autofocus taking advantage of the convergence speed and the stability in its optimization using a hybrid form of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-</sub> and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm. In the simulation, we focused on the suggestion of the proof of performance generality.