Full-color Images Research Articles

Achromatic metalenses for full visible spectrum with extended group delay control via dispersion-matched layers

Achieving achromaticity across the visible light spectrum is crucial for metalenses in imaging systems. Single-layer metalenses struggle with weak focusing power or small aperture sizes due to inadequate group delay control. Multilayer metalenses offer some improvement but come with increased design and fabrication complexity. Here, we demonstrate a strategy using meta-atoms with material layers engineered for matching dispersion, allowing large and fine adjustments of group delay. Our design substantially broadens the group delay range, allowing us to experimentally demonstrate several polarization-independent metalenses operating across the entire visible spectrum (400-700 nm). We design, fabricate, and characterize achromatic metalenses with aperture diameters of 16 μm, 66 μm, 200 μm, and 400 μm, and numerical apertures of 0.27, 0.11, 0.04, and 0.02, respectively. Among them, the 400-μm diameter, 0.02-numerical-aperture metalens is used to demonstrate full-color imaging capabilities. Our results exhibit diffraction-limited performance, high efficiency, and accurate full-color image reproduction.

A comparative evaluation of deep learning approaches for ophthalmology

There is a growing number of publicly available ophthalmic imaging datasets and open-source code for Machine Learning algorithms. This allows ophthalmic researchers and practitioners to independently perform various deep-learning tasks. With the advancement in artificial intelligence (AI) and in the field of imaging, the choice of the most appropriate AI architecture for different tasks will vary greatly. The best-performing AI-dataset combination will depend on the specific problem that needs to be solved and the type of data available. The article discusses different machine learning models and deep learning architectures currently used for various ophthalmic imaging modalities and for different machine learning tasks. It also proposes the most appropriate models based on accuracy and other important factors such as training time, the ability to deploy the model on clinical devices/smartphones, heatmaps that enhance the self-explanatory nature of classification decisions, and the ability to train/adapt on small image datasets to determine if further data collection is worthwhile. The article extensively reviews the existing state-of-the-art AI methods focused on useful machine-learning applications for ophthalmology. It estimates their performance and viability through training and evaluating architectures with different public and private image datasets of different modalities, such as full-color retinal images, OCT images, and 3D OCT scans. The article is expected to benefit the readers by enriching their knowledge of artificial intelligence applied to ophthalmology.

Exploring the relationship of colour categorization with pork colour standards under different subjective and objective conditions

The impact of using incorrect lighting while subjectively scoring pork colour with subjective standards (Japanese, Canadian, and Kodak grey) was explored. Lightness was more important than a good colour match between standards and meat. Subjective and image-based automated scoring with Canadian standards were correlated at 0.71–0.77 (P < 0.001) with significant differences in scale distribution (D = 0.14–0.46; P < 0.002), primarily with moderately dark meat. Automated scoring on full colour and greyscale images were strongly related (r = 0.83, P < 0.001) and showed matching score distributions when whole scores were used. Tracking automated colour categorization during blooming showed very good potential for reliable categorization after 1 min exposure to air for most meat colours, indicating that reliable automated on-line sorting of pork for colour is easily within reach.

Chemically and geometrically programmable photoreactive polymers for transformational humidity-sensitive full-color devices

Humidity-sensitive structural color has emerged as a promising technology due to its numerous advantages that include fast response, intuitiveness, stand-alone capability, non-toxicity, as well as resistance to thermal and chemical stresses. Despite immense technological advancements, these structural colors lack the ability to present independent multiple images through transformation. Herein, we present an approach to address this constraint by introducing a chemically and geometrically programmable photoreactive polymer which allows preparation of transformational humidity-sensitive full-color devices. Utilizing azido-grafted carboxymethyl cellulose (CMC-N3) allows adjustments in swelling properties based on the grafting ratio (Γ) of azido groups upon UV-induced crosslinking. Also, the distinctive photo-curability of the polymer enables precise geometric control to achieve vivid colors in combination with disordered plasmonic cavities. Our work culminates in the development of an advanced anti-counterfeiting multiplexer capable of displaying different full-color images with variation in humidity levels. The showcased color displays signify pivotal breakthroughs in tunable optical technologies, illustrating how chemical modifications in hydrogels provides additional degrees of freedom in the design of advanced optical devices.

Cr-Coot: Chronological Coot Algorithm-Based Deep Learning Model for Video Demosaicing

Usually, digital cameras comprise sensor arrays enclosed by Color Filter Arrays (CFAs), mosaics of minute color filters. Thus, every pixel sensor usually records limited spectral data regarding relevant pixels. Demosaicing is defined as the procedure of deducing the misplaced data for every pixel, which plays a vital role in recreating high-quality full-color images. Denoising and demosaicing are the major processes in the camera imaging chain for both videos and images. Here, reconstruction errors occur in these points and have undesirable effects on the final outcome, when it is not appropriately managed. The demosaicing process provokes color and spatial correlation of noises, and it is improved by means of imagining a pipeline. This organized noise usually destroys the quality of the image as well as fails to prevent accurate interpretation of an image. During the mitigation of this structured noise on processed data, denoising techniques diminish the texture and information. Therefore, an effectual demosaicing technique is essential for recreating the full-color image from the defective color samples. Thus, in this paper, an effectual video demosaicing model is proposed using an optimized deep learning system. The designed video demosaicing system achieved better performance with a Peak Signal-to-Noise Ratio (PSNR) of 59.74 dB, Second Derivative, like Measure of Enhancement (SDME) of 63.51, and Root Mean Squared Error (RMSE) of 0.3660.

An integrated electronic textile system capable of displaying full-color images and videos

An integrated electronic textile system capable of displaying full-color images and videos

Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising

ABSTRACT Demosaicking is a popular scientific area that is being explored by a vast number of scientists. Current digital imaging technologies capture colour images with a single monochrome sensor. In addition, the colour images were captured using a sensor coupled with a Colour Filter Array (CFA). Furthermore, the demosaicking procedure is required to obtain a full-colour image. Image denoising and image demosaicking are the two important image restoration techniques, which have increased popularity in recent years. Finding a suitable strategy for multiple image restoration is critical for researchers. Hence, a deep learning (DL) based image denoising and image demosaicking is developed in this research. Moreover, the Autoregressive Circle Wave Optimization (ACWO) based Demosaicking Convolutional Neural Network (DMCNN) is designed for image demosaicking. The Quantum Wavelet Transform (QWT) is used in the image denoising process. Similarly, Quantum Wavelet Transform (QWT) is used to analyse the abrupt changes in the input image with noise. The transformed image is then subjected to a thresholding technique, which determines an appropriate threshold range. Once the threshold range has been determined, soft thresholding is applied to the resulting wavelet coefficients. After that, the extraction and reconstruction of the original image is carried out using the Inverse Quantum Wavelet Transform (IQWT). Finally, the fused image is created by combining the results of both processes using a weighted average. The denoised and demosaicked images are combined using the weighted average technique. Furthermore, the proposed QWT+DMCNN-ACWO model provided the ideal values of Peak signal-to-noise ratio (PSNR), Second derivative like measure of enhancement (SDME), Structural Similarity Index (SSIM), Figure of Merit (FOM) of 0.890, and computational time of 49.549 dB, 59.53 dB, 0.963, 0.890, and 0.571, respectively.

Large-scale high-quality full-color computer-generated volume hologram fabricated by the stacking and tiling technique.

A technique is presented to produce very high-quality full-color holographic 3D images in large-scale computer holography, which uses over a billion pixels. In this technique, three large-scale computer-generated holograms, printed using laser lithography, are transferred to three computer-generated volume holograms (CGVH) using a method called tiling contact-copy. Then, the full-color holographic image is created by stacking the three CGVHs. We demonstrate a 10-cm square stacked CGVH that reconstructs full-parallax full-color 3D images at high quality with a viewing angle of more than 35°.

Auroral breakup detection in all-sky images by unsupervised learning

Abstract. Due to a large number of automatic auroral camera systems on the ground, image data analysis requires more efficiency than what human expert visual inspection can provide. Furthermore, there is no solid consensus on how many different types or shapes exist in auroral displays. We report the first attempt to classify auroral morphological forms by an unsupervised learning method on an image set that contains both nightside and dayside aurora. We used 6 months of full-colour auroral all-sky images captured at a high-Arctic observatory on Svalbard, Norway, in 2019–2020. The selection of images containing aurora was performed manually. These images were then input into a convolutional neural network called SimCLR for feature extraction. The clustered and fused features resulted in 37 auroral morphological clusters. In the clustering of auroral image data with two different time resolutions, we found that the occurrence of 8 clusters strongly increased when the image cadence was high (24 s), while the occurrence of 14 clusters experienced little or no change with changes in input image cadence. We therefore investigated the temporal evolution of a group of eight “active aurora” clusters. Time periods for which this active aurora persisted for longer than two consecutive images with a maximum cadence of 6 min coincided with ground-magnetic deflections, and their occurrence was found to maximize around magnetic midnight. The active aurora onsets typically included vortical auroral structures and equivalent current patterns typical for substorms. Our findings therefore suggest that our unsupervised image clustering method can be used to detect auroral breakups in ground-based image datasets with a temporal accuracy determined by the image cadence.

Correction of the wavelength error in transmission of a full-color holographic 3D image

When a digital holographic image represented by a sampled wavefield is transmitted and the wavelength used in the three-dimensional (3D) display devices does not agree exactly with the wavelength of the original image data, the reconstructed 3D image will differ slightly from the original. This slight change is particularly problematic for full-color 3D images reconstructed using three wavelengths. A method is proposed here to correct the holographic image data and reduce the problems caused by wavelength mismatch. The effectiveness of the method is confirmed via theoretical analysis and numerical experiments that evaluate the reconstructed images using several image indices.

Holoscopic Elemental-Image-Based Disparity Estimation Using Multi-Scale, Multi-Window Semi-Global Block Matching

In Holoscopic imaging, a single aperture is used to acquire full-colour spatial images like a fly’s eye by gently altering angles between nearby lenses with a micro-lens array. Due to its simple data collection and visualisation methods, which provide robust and scalable spatial information, and its motion parallax, binocular disparity, and convergence, this technique may be able to overcome traditional 2D imaging issues like depth, scalability, and multi-perspective problems. A novel disparity-map-generating method uses angular information from a single Holoscopic image’s micro-images, or Elemental Images (EIs), to create a scene’s disparity map. Not much research has used EIs instead of Viewpoint Images (VPIs) for disparity estimation. This study investigates whether angular perspective data may replace spatial orthographic data. Using noise reduction and contrast enhancement, EIs with a low resolution and lack of texture are pre-processed to calculate the disparity. The Semi-Global Block Matching (SGBM) technique is used to calculate the disparity between EI pixels. A multi-resolution approach overcomes EIs’ resolution constraints, and a content-aware analysis dynamically modifies the SGBM window size settings to generate disparities across different texture and complexity levels. A background mask and nearby EIs with accurate backgrounds detect and rectify EIs with erroneous backgrounds. Our method generates disparity maps that outperform two state-of-the-art deep learning algorithms and VPIs in real images.

Advancing infrared display technology with carbon nanotube-embedded spandex fibers

We report on developing a wearable infrared (IR) display based on stretchable conductive fibers fabricated through an expansion–contraction process. The expansion process creates a gap between the strands of spandex fibers. This is achieved by immersing the fibers in a solvent where carbon nanotubes (CNTs) are dispersed, thereby embedding the CNTs. Contraction is achieved through a drying process, which removes the gap between the strands of the spandex fibers. This ensures that the CNTs remain embedded, even after repeated stretching. The CNT-embedded spandex fibers are arranged into a 5 × 7 pixel array. The intensity of the IR rays emitted from the fibers can be controlled by adjusting their temperature, which is achieved by varying the driving voltage. Full-color IR images and displays of letters and numbers are realized through precise control of the IR light intensity. The wearable IR display developed in this study opens up exciting possibilities for integration into advanced systems such as military identification, artificial intelligence robots, autonomous driving, and aerospace industry applications.

Enhancing in full-color single-pixel imaging: integrating variable density sampling with hyper-Laplacian priors

Full-color single-pixel imaging aims to restore chromatic images using a single detector element, such as a photodiode or a single-pixel camera. However, image quality is inevitably compromised at low sampling rates due to inefficient sampling methods or incomplete representation of spectrum information. To address these challenges, we meticulously consider the distribution of the image frequency spectrum and the correlation between multiple bands and make further improvements in sampling strategy and reconstruction methods. First, we propose a variable density random sampling strategy based on the exponential distribution to enhance image sampling efficiency. Second, we discover that in most cases, there exists a hyper-Laplacian distribution between spectral mixed images and monochromatic images. Building upon this observation, we designed a hyper-Laplacian prior and seamlessly integrated it into our reconstruction method to enhance the performance of full-color images. Experimental results demonstrate that our method significantly improves the quality of reconstructed full-color images compared to state-of-the-art methods.

An Efficient VLSI Architecture for Bi-Cubic Interpolation using Carry Skip Adder

In any signal processing system, signal distortion can occur. A compensation scheme would adjust the captured signal to correct for any deviations and ensure accurate signal transmission and preservation. A compensation scheme is used to correct such deviations for mostly linear is used to enhance the quality of reconstructed signal suitable for high-quality super-resolution techniques. This technique is applicable for multidimensional signal processing such as image processing. Except for the resolution of the acquired images, the extracting of colour information is greatly affected by the number of pixels. This scheme enhances the correlation of interpolated pixels and their immediate neighbouring pixels. This research aims to develop a super-resolution demosaicking (SRD) technique to reconstruct high-resolution, full-colour images from samples of the same previously seen (in-painted) image without a sophisticated, time-consuming training process. Hardware-oriented colour demosaicking methods, which give importance to green colour pixels, have thus enhanced the quality of the restored image. A Boundary Mirror and Detector mechanism also considered here for improving the pixel quality up to the boundaries. Three interpolators were economized by using a method called hardware sharing. The present Bi-Cubic interpolation technique has been designed as part of our overall effort to enhance and advance the SRD approach toward better performance and broader acceptance. This had been implemented by VLSI architecture and VHDL, whereas MATLAB had executed the image conversion and de-conversion methods. Comparison in the evaluation of results was done based on PSNR and SSIM values, followed by the analysis of the output results after the synthesis process using a Xilinx Vivado ZYNQ-7-ZC702 FPGA.

Enhancing the Performance of Optical Hardware through Software Applications: A Study

The process of image demosaicing, essential for reconstructing fullcolour images from incomplete colour samples obtained from a colourfilter array (CFA), has garnered renewed interest, particularly with the prevalent use of the Bayer pattern. This resurgence can be attributed to the growing availability of source codes and executables, facilitating reproducible research in the field. In this article, a thorough survey encompassing over fifty published works on demosaicing since 1999. The objective is to complement prior reviews and provide insights into the evolution and current landscape of demosaicing techniques. By addressing key issues and delineating fundamental differences among various approaches, a thorough overview of the field is made. A notable finding from the survey is the popularity of spatial demosaicing methods, where the initial step involves interpolating the luminance channel, followed by reconstructing the chrominance channels based on the recovered luminance information. The article highlights three crucial areas requiring additional research. Firstly, addressing the difficulty of demosaicking images with low spectral correlation is paramount, along with enhancing our comprehension of the trade-off between spatial correlation and spectral correlation. It is also found that there is a necessity for more comprehensive investigation into evaluating the performance of demosaicking algorithms, particularly concerning the utilization of reference images, and understanding supplementary components within the imaging pipeline.

Determining Customer Preferences in Choosing a Marketplace Using the Conjoint Analysis Method

The number of online shopping transactions in Indonesia has grown over the last ten years by 17% and the total number of e-commerce businesses has reached 26.2 million units. This creates competition that requires companies to maintain their existence by understanding consumer psychology. This study aims to determine the combination of attribute levels that are most preferred by consumers in choosing a marketplace as a place to shop by using conjoint analysis. The data used is the result of a survey of Riau University students in the form of a questionnaire. The results of the conjoint analysis in this study show that the level of each attribute that respondents prefer is the display of full color applications and images, free shipping promotions on a certain amount of purchase, the method of paying Cash On Delivery (COD), using J&amp;T/JNE/Sicepat/Tiki/Pos delivery services, and product reviews are available in the form of photos and videos.

Complex RGB spatial light modulation with a dual-layer in-plane switching liquid crystal panel

Complex RGB spatial light modulators are required to produce full-color holographic displays. In particular, complex spatial light modulation, which modulates the amplitude and phase of incident light is essential for noiseless dynamic computer-generated hologram synthesis. The feasibility of full-color holographic image generation through the dual-layer in-plane switching liquid crystal plane is theoretically validated and experimentally demonstrated.

Full-color optical combiner with good imaging quality and a wide angle of incident light acceptance.

This work demonstrates a full-color optical image combiner for augmented reality head-up displays. In this device, the angle of the incident light of the computer-generated image reflected by it can vary over a large range. It achieves improved performance by using a combination of polarization interference filters (PIFs), quarter-wave retarders (QWRs), and polarization volume holograms (PVHs). The details for an example design and its performance are presented.

Исследование влияния коэффициента усиления на степень искажения и эффективность извлечения цифрового водяного знака на полноцветные изображения методом дискретных преобразований

The problem of applying a digital watermark based on discrete wavelet and co-sine transformations method to a full-color image is considered. This method is a procedure for embedding a label into the frequency domain of a digital image in the algorithm of which a certain gain is used. General information about the algorithm and its components is presented. The influence of the amplifying coefficient on the effectiveness of the introduction and extraction of the watermark and the degree of distortion of the processed image is described. Studies of these dependencies have been carried out in order to find the optimal spectrum (coefficient) of the digital color space for the introduction of a hidden label into a full-color image with an optimal gain value. The Pearson linear correlation coefficient was used to measure the degree of extraction efficiency, and the metric of the index of structural similarity of digital images was used to measure the degree of distortion. The spectra that are most suitable as a digital watermark carrier were identified, as well as the optimal values of the amplifying coefficient for these spectra. The use of the optimal coefficient does not lead to significant distortion of the image, while having sufficient efficiency of watermark extraction.

Monolithic full-color active-matrix micro-LED micro-display using InGaN/AlGaInP heterogeneous integration

A prototype of full-color active-matrix micro-light-emitting diode (micro-LED) micro-display with a pixel density of 391 pixel per inch (ppi) using InGaN/AlGaInP heterogeneous integration is demonstrated. InGaN blue/green dual-color micro-LED arrays realized on a single metal organic chemical vapor deposition (MOCVD)-grown GaN-on-Si epiwafer and AlGaInP red micro-LED arrays are both monolithically fabricated, followed by the integration with a common complementary metal oxide semiconductor (CMOS) backplane via flip-chip bonding technology to form a double-layer thin-film display structure. Full-color images with decent color gamut and brightness are successfully displayed through the fine adjustment of driving current densities of RGB subpixels. This full-color display combines the advantages of high quantum efficiency of InGaN material on blue/green light and AlGaInP material on red light through heterogeneous integration and high pixel density through monolithic fabrication approach, demonstrating the feasibility and prospects of high brightness, good color performance, and high-resolution micro-LED micro-displays in future metaverse applications.