Color Filter Research Articles

CAD-PsorNet: deep transfer learning for computer-assisted diagnosis of skin psoriasis

Psoriasis, being a chronic, inflammatory, lifelong skin disorder, has become a major threat to the human population. The precise and effective diagnosis of psoriasis continues to be difficult for clinicians due to its varied nature. In northern India, the prevalence of psoriasis among adult population ranges from 0.44 to 2.8%. Chronic plaque psoriasis accounts for over 90% of cases. This study utilized a dataset of 325 raw images collected from a reputable local hospital using a digital camera under uniform lighting conditions. These images were processed to generate 496 image patches (both diseased and normal), which were then normalized and resized for model training. An automated psoriasis image recognition framework was developed using four state-of-the-art deep transfer learning models: VGG16, VGG19, MobileNetV1, and ResNet-50. The convolutional layers adopted various edge, shape, and color filters to generate the feature map for psoriasis detection. Each pre-trained model was adapted with two dense layers, one dropout layer, and one output layer to classify input images. Among these models, MobileNetV1 achieved the best performance, with 94.84% sensitivity, 89.37% specificity, and 97.24% overall accuracy. Hyper-parameter tuning was performed using grid search to optimize learning rates, batch sizes, and dropout rates. The AdaGrad (Adaptive gradient)) optimizer was chosen for its adaptive learning rate capabilities, facilitating quicker convergence in model performance. Consequently, the methodology’s performance improved to 94.25% sensitivity, 96.42% specificity, and 99.13% overall accuracy. The model’s performance was also compared with non-machine learning-based diagnostic methods, yielding a Dice coefficient of 0.98. However, the model’s effectiveness is dependent upon high-quality input images, as poor image conditions may affect accuracy, and it may not generalize well across diverse demographics or psoriasis variations, highlighting the need for varied training datasets for robustness.

In Situ Solid-State Dewetting of Ag-Au-Pd Alloy: From Macro- to Nanoscale.

Metal alloy nanostructures represent a promising platform for next-generation nanophotonic devices, surpassing the limitations of pure metals by offering additional "buttons" for tailoring their optical properties by compositional variations. While alloyed nanoparticles hold great potential, their scalability and underexplored optical behavior still limit their application. Here, we establish a systematic approach to quantifying the unique optical behavior of the AgAuPd ternary system while providing a direct comparison with its pure constituent metals. Computationally, we analyze their electronic structure and uncover the transition of Pd d states to Pd/Ag hybridized s states in the bulk form, explaining the similar optical properties observed between Pd and AgAuPd. Experimentally, we fabricate pure metal and fully alloyed nanoparticles through solid-state dewetting, a scalable method. During the process, we trace the optical transition in the systems from the initial thin film stage to the final nanoparticle stage with in situ ellipsometry. We reveal the interplay between optical properties influenced by chemical interdiffusion and localized surface plasmon resonance arising from morphological changes with ex situ surface characterizations. Additionally, we analytically implement a metallic layer derived from the ternary system in a trilayer device, resulting in a single-time and irreversible color filter, to demonstrate an application encompassing a lithography-free and cost-effective route for nanophotonic devices.

Research and fabrication of color filters based on porous anodic alumina

Research and fabrication of color filters based on porous anodic alumina

Switchable Camouflage via Reflective Display

In nature, dynamic camouflage is performed by cephalopods and reptiles. Humans attempt to perform dynamic camouflage by employing display devices to show the surrounding background. In this work, a switchable camouflage device based on an electrophoretic display (EPD) is proposed. Color‐filter EPDs display colors by reflecting light through the color filters and black‐and‐white EPDs. The number of subpixels is found to be an important factor on color performance. To improve the poor saturation of color‐filter EPDs, the number of color filter subpixels is reduced. Compared with filters with three and four subpixels, a dual‐subpixel filter proposed in this work significantly improves the average saturation of red, green, and blue colors, with increases of 49% and 112%, respectively. Subsequently, the spectral characteristics of the color filter and black‐and‐white EPD are optimized by using genetic algorithm to reduce the average color difference between the display and the switchable target color, which can be reduced as low as 0.18. To visually demonstrate the color reproduction capability of the dual‐subpixel EPD, sample applications including the switchable vegetation and digital camouflages are designed and have a high degree of agreement with the background. In this work, an innovative and effective approach is introduced to dynamic camouflage.

New Yellow Azo Pyridone Derivatives with Enhanced Thermal Stability for Color Filters in Image Sensors

Two new yellow azo pyridone derivatives, (E)-6-hydroxy-1-(3-methoxypropyl)-4-methyl-2-oxo-5-(p-tolyldiazenyl)-1,2-dihydropyridine-3-carbonitrile (APY-M) and 5,5′-((1E,1′E)-(methylenebis(4,1-phenylene))bis(diazene-2,1-diyl))bis(6-hydroxy-4-methyl-2-oxo-1,2-dihydropyridine-3-carbonitrile) (APY-D), were designed and synthesized as yellow colorants for image sensors. The properties of these new compounds were evaluated in both solution and color filter film forms, focusing on their optical and thermal characteristics. The molar extinction coefficient values of APY-M and APY-D in solution were found to be 2.7 × 105 and 3.0 × 105 L/mol·cm, respectively. The transmittance of the newly synthesized compounds met commercial requirements, showing values below 0.21% at 435 nm and above 97.1% at 530 nm. APY-D exhibited a molar extinction coefficient value in solution that was 1.15 times higher than that of the commercially used yellow colorant Disperse Yellow 241. Both newly synthesized compounds satisfied the decomposition temperature requirement of over 230 °C, which is essential for the color filter manufacturing process in image sensors. In particular, APY-D, with its dimeric structure and increased molecular weight, demonstrated enhanced thermal stability, with a 50 °C increase in decomposition temperature compared to Disperse Yellow 241. Color filter films for image sensors were fabricated using the new compounds, and their thermal resistance was evaluated. APY-D maintained its transmittance due to the enhanced thermal stability provided by its dimer structure and increased molecular weight. Consequently, APY-D is anticipated to be a promising candidate for use as a yellow colorant in image sensors, owing to its excellent optical and thermal properties.

Parallel Lossless Compression of Raw Bayer Images on FPGA-Based High-Speed Camera

Digital image compression is applied to reduce camera bandwidth and storage requirements, but real-time lossless compression on a high-speed high-resolution camera is a challenging task. The article presents hardware implementation of a Bayer colour filter array lossless image compression algorithm on an FPGA-based camera. The compression algorithm reduces colour and spatial redundancy and employs Golomb–Rice entropy coding. A rule limiting the maximum code length is introduced for the edge cases. The proposed algorithm is based on integer operators for efficient hardware implementation. The algorithm is first verified as a C++ model and later implemented on AMD-Xilinx Zynq UltraScale+ device using VHDL. An effective tree-like pipeline structure is proposed to concatenate codes of compressed pixel data to generate a bitstream representing data of 16 parallel pixels. The proposed parallel compression achieves up to 56% reduction in image size for high-resolution images. Pipelined implementation without any state machine ensures operating frequencies up to 320 MHz. Parallelised operation on 16 pixels effectively increases data throughput to 40 Gbit/s while keeping the total memory requirements low due to real-time processing.

Bacterial Hybrid Light-Emitting Diodes.

Photon down-converting filters with fluorescent proteins (FPs) are a new frontier in the quest for rare-earth-free and non-toxic color filters for white light-emitting diodes. There are, however, concerns related to the FP purification costs and lack of FP recyclability/reuse. Here, the direct use of bacteria in photon down-converting filters can be of utmost relevance, eliminating purification and allowing in situ production of new FPs. However, their high background autofluorescence/scattering and low stability in polymer coatings have traditionally hampered the application of Engineering Living Materials (ELMs) for photon manipulation. Indeed, there are no examples of ELMs in lighting systems. This work discloses the first protocol to prepare living spheroplasts with > 90% scattering reduction, high FP expression fairly keeping their photoluminescence figures-of-merit, and excellent resilience in polymer films over 1 year under ambient storage. This unlocked the preparation of the first bacteria hybrid light-emitting diodes integrating ELMs for photon conversion. These devices feature similar stabilities to those using purified FPs, while enabling a cost-effective strategy and active FP recycling by the simple recultivation of spheroplasts. Overall, this work introduces a successful case toward bacteria-polymer photon manipulation, in general, and a new living lighting concept, in particular.

Multi-stage and multi-colour liquid crystal reflections using a chiral triptycene photoswitchable dopant.

The photomodulation of the helical pitch of cholesteric liquid crystals results in dynamic and coloured canvases that can potentially be used in applications ranging from energy-efficient displays to colour filters, anti-counterfeiting tags and liquid crystal (LC) lasers. Here we report on the analysis of a series of photoswitchable chiral dopants that combine the large geometrical change and bistability of hydrazone switches with the efficient helical pitch induction of the chiral motif, triptycene. We elucidate the effects that conformational flexibility, dispersion forces and π-π interactions have on the chirality transfer ability of the dopant. We then use the irradiation time with visible light (442 nm) combined with a simple digital light processing microscope projection set-up to draw numerous stable multi-coloured images on an LC canvas, showcasing the fine control this dopant yields over the LC assembly.

Investigating the Influence of Wavelength-Specific Textured Backgrounds in Background Oriented Schlieren (BOS) Imaging

This study investigates the influence of wavelength-specific textured backgrounds on the effectiveness of Background-Oriented Schlieren (BOS) imaging, focusing on wavelengths from 400 nm to 670 nm at intervals of 30 nm intervals and multiple captured recordings for each background wavelength interval. By analyzing the signal-to-noise ratio (SNR) computationally, and the image gradient magnitude, we aimed to determine the optimal wavelengths for capturing turbulence and determine the effectiveness of colored backgrounds in natural external environments for schlieren. The SNR, calculating the ratio of mean signal intensity to noise standard deviation, revealed the highest value at 550 nm (SNR = 22.8), indicating maximized clarity. Similarly, image gradient magnitude, computed using the Sobel operator to assess spatial intensity changes, peaked at 550 nm (G=52.3), confirming effective turbulence visualization. Our findings align with the Bayer color filter trend, suggesting that the green spectrum is particularly advantageous for BOS imaging. Deviations at 490 nm and 580 nm, characterized by lower SNR and gradient magnitude, could be attributed to atmospheric scattering, refractive index overlap, or slight digital video capture differences., highlighting environmental factors that can influence imaging performance and value variation. These insights emphasize the importance of wavelength selection and background design in real-world BOS applications, suggesting that while 550 nm provides optimal results, further refinement may enhance the effectiveness of other wavelengths.

Improving the color gamut of gold nanostructures using vertical tandem nano-disks

Metallic nanoparticles have been used in structural coloration applications since they provide an alternative for the synthesis of color filters, with extended color gamut, enhanced color saturation, and brightness. These optical properties can be further improved when integrating nanoparticles into coupled dimers that give rise to enhanced hybridized plasmon resonances. In this contribution, gold nano-disks are split into Au/SiO2/Au layered structures to enhance its plasmon resonances and evaluate its improved performance as reflective color filters. To do so, the reflectance spectra of the layered disks were obtained with numerical simulations, identifying high amplitude peaks in the visible band and low-amplitude peaks in the near infrared one, related to the hybridized resonances. By choosing the appropriate size of the intermediate dielectric and metallic layers, as well as the near-field coupling, peaks can be tuned along the visible and near infrared bands, respectively. The chromatic coordinates of the addressed nanostructures obtained from the reflectance spectra show that the nanostructures hold color gamut that extends toward magenta, orange, yellowish, and reddish shades of colors.

Sustainable scheduling of TFT-LCD cell production: A hybrid dispatching rule and two-phase genetic algorithm

TFT-LCD manufacturing process involves Array, Color Filter (CF), Cell, and Module. The Cell process is pivotal in the TFT-LCD production supply chain; it bridges the front-end (Array and CF) and back-end (Module), maintaining a balance of production capacities. Supply from the front-end is transmitted to the back-end, ensuring prompt responsiveness to customer demands. As awareness of environmental conservation grows, implementing sustainable development in the TFT-LCD manufacturing industry has become crucial. Specifically, reducing the machine setup frequency in TFT-LCD scheduling decreases energy consumption during machine operation and wait times and prolongs equipment lifespan by minimizing wear from repeated shutdowns. To tackle the intricate scheduling challenges within TFT-LCD production, this study proposes an intelligent, sustainable scheduling approach that employs a rolling dispatching and line balancing mechanism and a two-phase genetic algorithm (HDTPGA) for Cell scheduling. The goal is to minimize makespan considering product diversity, production constraints, work-in-process balancing, and machine setup frequency. This holistic approach promotes energy-efficient and sustainable manufacturing practices. According to the empirical study, the proposed HDTPGA reduces the makespan by approximately 10% compared to the baseline. Additionally, it reduces the average daily setup frequency of bottleneck stages (ODF and CCT) in the Cell process by approximately 68.25% and 66.25%, respectively. The main contribution of this paper is to propose an intelligent scheduling algorithm to optimize Cell production scheduling in practice. The HDTPGA achieves equilibrium between front-end and back-end production, utilizing an efficient line change mechanism to reduce energy consumption. The enhancement in efficiency not only restricts material waste but also reduces the environmental impact, enabling manufacturers to adopt green production practices and promoting a transition of the entire panel industry toward sustainability.

Analysis of the Influence of the Size of Color-Calibrated Schlieren Filters on the General Sensitivity of Quantitative Schlieren Systems

The quantitative color schlieren technique is renowned for its capacity to convert deflection angles into color ratios. This technique has been instrumental in providing data on 2D flows. The current study delves into assessing how the geometry and optical characteristics of color filters impact the sensitivity of the schlieren system. At present, there are many papers making the assumption that implementing a larger-sized color filter leads to better system sensitivity. However, having more calibration filter positions can lead to measurement errors due to the difficult calibration process. The present investigation focuses on the type of color filters created with a gradual evolution of colors. A turbulent, round water vapor jet serves as the test case. By comparing the results obtained with two different filter sizes, this analysis gives insight into the compromises made between system sensitivity and ease of calibration, helping one to better understand the trade-offs between the above-mentioned parameters. Moreover, the quantitative and qualitative results of the test case are presented to offer more comprehensive insights into quantitative color-calibrated schlieren.

Seeing through fire with one pixel

Seeing through fire is a critical capability in various applications, such as fire rescue, industrial combustion monitoring, and scientific research. However, the intense electromagnetic radiation emitted by flames can saturate and blind conventional imaging systems, making it challenging to visualize objects or scenes obscured by fire. In this paper, we present a novel method for real-time, full-color through-flame imaging using structured illumination and single-pixel detection. By projecting a series of carefully designed light patterns onto the scene and measuring the backscattered light with a single-pixel detector, we can computationally reconstruct the obscured scene while effectively suppressing the flame's contribution to the image. The single-pixel detector's high dynamic range and sensitivity enable it to capture the weak backscattered signal without being overwhelmed by the flame's intense radiation. We demonstrate the method's effectiveness in several experiments, showcasing its ability to image static and dynamic scenes through both steady and turbulent flames at a frame rate of 15 Hz. Furthermore, we show that the proposed method can be extended to full-color imaging by using three single-pixel detectors with different color filters. The results highlight the potential of this approach for enhancing visibility in fire-related scenarios and other challenging imaging conditions. We believe that the integration of this technology into augmented reality systems could provide firefighters and other users with valuable real-time visual information, improving situational awareness and decision-making in critical situations. We also believe the method can be used for seeing through any self-luminous objects.

Joint learning of RGBW color filter arrays and demosaicking

RGBW color filter arrays (CFAs) have gained widespread attention for their superior performance in low-light conditions. Most existing demosaicking methods are tailored for specific RGBW CFAs or involve manual joint design with CFAs. The close relationship between sampling and reconstruction means that restricting the search space through predefined CFAs and demosaicking methods severely limits the ability to achieve optimal performance. In this paper, we propose a new approach for joint learning of RGBW CFA and demosaicking. This approach can simultaneously learn optimal CFAs and demosaicking methods of any size, while also being capable of reconstructing mosaicked images of any size. We use a surrogate function and arbitrary CFA interpolation to ensure end-to-end learning of the RGBW CFA. We also propose a dual-branch fusion reconstruction network that utilizes the W channel to guide the reconstruction of R, G, and B channels, reducing color errors while preserving more image details. Extensive experiments demonstrate the superiority of our proposed method. Our code is available at: https://github.com/fql0528/learncfa.

Inverse design of ultranarrow and high-efficiency color filters based on tandem convolutional neural networks

In this paper, an ultranarrow, high-efficiency color filter based on a two-dimensional grating nanostructure array is realized by a tandem inverse design network. The tandem inverse designed network is generated by connecting an inverse network with a pre-trained forward network. The tandem convolutional neural network successfully overcame the non-uniqueness problem of the training data. The performance of the networks is verified by a test set of 2592 data points, and the average MSE of the prediction results of the tandem network is 1.01 × 10−3. Extremely low error indicates that the tandem network can learn and capture the distribution characteristics of the transmission spectrum and give a reasonable design of the structural parameters. Finally, we obtained multiple inverse-designed transmissive color filters with ultranarrow linewidths and high efficiency, with central wavelengths between 480 nm and 630 nm. The linewidth and efficiency of each kind of color filter are less than 10.6 nm and higher than 60%. Narrow linewidths and wide wavelength tunability result in high color accuracy and a broad color gamut of the filters. The filters with two-dimensional metallic nanostructure array also show polarization-independent characteristics. Compared with the traditional method, this method can quickly and accurately design the structural parameters corresponding to the spectral response, which provides a new method for the nanostructure design.

An Active-Matrix Synaptic Phototransistor Array for In-Sensor Spectral Processing.

The human retina perceives and preprocesses the spectral information of incident light, enabling fast image recognition and efficient chromatic adaptation. In comparison, it is reluctant to implement parallel spectral preprocessing and temporal information fusion in current complementary metal-oxide-semiconductor (CMOS) image sensors, requiring intricate circuitry, frequent data transmission, and color filters. Herein, an active-matrix synaptic phototransistor array (AMSPA) is developed based on organic/inorganic semiconductor heterostructures. The AMSPA provides wavelength-dependent, bidirectional photoresponses, enabling dynamic imaging and in-sensor spectral preprocessing functions. Specifically, near-infrared light induces inhibitory photoresponse while UV light results in exhibitory photoresponse. With rational structural design of the organic/inorganic hybrid heterostructures, the current dynamic range of phototransistor is improved to over 90 dB. Finally, a 32 × 64 AMSPA (128 pixels per inch) is demonstrated with one-switch-transistor and one-synaptic phototransistor (1-T-1-PT) structure, achieving spatial chromatic enhancement and temporal trajectory imaging. These results reveal the feasibility of AMSPA for constructing artificial vision systems.

Local color display technology

AbstractThe local color display technology utilizes a high refresh rate LCD open‐cell without a color filter and enables color adjustment through independent RGB backlight zones isolated by an anti‐scatter grid (ASG). Synchronized calibration between the open‐cell and the backlight zones is achieved by aligning the backlight controller with the vertical clock pulse (CPV)/vertical start pulse (STV) signals in the open‐cell gate driver, thereby determining the open‐cell scan line position. Once synchronous calibration is completed, the backlight controller refreshes the corresponding RGB backlight zones whenever the open‐cell scan line passes through them. The local color display technology can reduce the cost of open‐cell by 20%, improve resolution by three times, enhance brightness by eight times, expand the color gamut to BT2020, and improve grayscale to 32‐bits.

Investigation and comparison of the influence of modified DBR and yellow color filters for quantum dot color conversion-based micro LED applications

This study compares how a modified distributed Bragg reflector (DBR) and yellow color filter (Y-CF) increase the color purity, viewing angle, and brightness of the quantum dot color conversion layer (QDCC) for micro-LED displays. We designed and built a 53-layer high-performance modified DBR with almost total blue leakage filtering (T %: 0.16 %) and very high G/R band transmittance (T %: 96.97 %) for comparison. We also use a Y-CF that filters blue light (T %: 0.84 %) and has good G/R band transmittance (T %: 94.83 %). Due to DBR's angle dependency effect, the modified DBR/QDCC structure offers a remarkable color gamut (117.41 % NTSC) at the forward viewing angle, but this rapidly diminishes beyond 30°. The Y-CF/QDCC structure retains 116 % NTSC color at all viewing angles. Because of its consistent color performance at all viewing angles, sufficient brightness, and outstanding color gamut, the Y-CF/QDCC structure is the best option for contemporary QDCC-based micro-LED displays.

An Edge-Preserving Regularization Model for the Demosaicing of Noisy Color Images

This paper proposes an edge-preserving regularization technique to solve the color image demosaicing problem in the realistic case of noisy data. We enforce intra-channel local smoothness of the intensity (low-frequency components) and inter-channel local similarity of the depth of object borders and textures (high-frequency components). Discontinuities of both the low-frequency and high-frequency components are accounted for implicitly, i.e., through suitable functions of the proper derivatives. For the treatment of even the finest image details, derivatives of first, second, and third orders are considered. The solution to the demosaicing problem is defined as the minimizer of an energy function, accounting for all these constraints plus a data fidelity term. This non-convex energy is minimized via an iterative deterministic algorithm, applied to a family of approximating functions, each implicitly referring to geometrically consistent image edges. Our method is general because it does not refer to any specific color filter array. However, to allow quantitative comparisons with other published results, we tested it in the case of the Bayer CFA and on the Kodak 24-image dataset, the McMaster (IMAX) 18-image dataset, the Microsoft Demosaicing Canon 57-image dataset, and the Microsoft Demosaicing Panasonic 500-image dataset. The comparisons with some of the most recent demosaicing algorithms show the good performance of our method in both the noiseless and noisy cases.

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.