Abstract The manifestation of color is either obtained through intrinsic quantum mechanical properties of materials (absorption or fluorescence) or through structural materials at the scale corresponding to the wavelength of light. Particularly, structural colors can occur with ultrathin layers, and their properties are modulated by nano‐structuring (photonic crystals and plasmonics), garnering much interest. However, the fabrication complexity and costs to miniaturize color devices and increase performance pose challenges. Here, a sub‐µm metal‐insulator‐metal color filter array (CFA) is reported based on the electron‐beam lithography resist function of silk protein. High‐resolution CFAs are implemented by depositing silver on grayscale patterned silk layers without complex and intricate nano‐structuring. Precise control of the electron‐beam dosage allows engineering of the desired color, and high quality‐factors (Q‐factors) lead to an extensive color gamut. Moreover, the light is well‐confined even when the pixel size is reduced to 300 nm. Consequently, the famous painting The Starry Night is realized as an 80k dots per inch (DPI) resolution CF display. In addition, secured information through color noising is generated by incorporating the CFA. This study presents a new way to add multispectral depth to future research in bioimaging, biosensors, displays, and information security.

Medical endoscopic video processing requires real-time execution of color component acquisition, color filter array (CFA) demosaicing, and high dynamic range (HDR) compression under low-light conditions, while adhering to strict thermal constraints within the surgical handpiece. Traditional hardware-aware neural architecture search (NAS) relies on fixed hardware design spaces, making it difficult to balance accuracy, power consumption, and real-time performance. A collaborative “power-accuracy” optimization method is proposed for hardware-aware NAS. Firstly, we proposed a novel hardware modeling framework by abstracting FPGA heterogeneous resources into unified cell units and establishing a power–temperature closed-loop model to ensure that the handpiece surface temperature does not exceed clinical thresholds. In this framework, we constrained the interstage latency balance in pipelines to avoid routing congestion and frequency degradation caused by deep pipelines. Then, we optimized the NAS strategy by using pipeline blocks and combined with a hardware efficiency reward function. Finally, color component acquisition, CFA demosaicing, dynamic range compression, dynamic precision quantization, and streaming architecture are integrated into our framework. Experiments demonstrate that the proposed method achieves 2.8 W power consumption at 47 °C on a Xilinx ZCU102 platform, with a 54% improvement in throughput (vs. hardware-aware NAS), providing an engineer-ready lightweight network for medical edge devices such as endoscopes.

This article investigates the influence of laser radiation on CCD and CMOS sensors in video cameras integrated into unmanned aerial vehicles (UAVs), with implications for counter-drone systems. It examines interaction mechanisms between laser beams and semiconductor structures, encompassing photoelectric effects, thermal degradation, and electrical disruptions. Threshold laser power levels are quantified, identifying points where functional impairments and permanent damage occur. A comparative analysis highlights the differential sensitivity and resilience of CCD versus CMOS sensors: CCDs exhibit lower damage thresholds (1.6-2.7 J/cm² at 532 nm for nanosecond pulses) due to sequential charge transfer, leading to linear defects, while back-side illuminated (BSI) CMOS sensors withstand up to 103 J/cm² owing to thinner active layers and pixel-level electronics. Key vulnerability factors are dissected, including laser wavelength (optimal 300-1100 nm range, with 532 nm for dazzling and 1064 nm for deep penetration damage), pulse duration (picosecond pulses induce nonlinear effects like two-photon absorption, reducing thresholds to 10 mJ/cm²), repetition rate (100 kHz lowers thresholds by 20-30% via cumulative heating), exposure time, and optical system properties. Literature reviews synthesize prior studies on optoelectronic vulnerabilities, while laboratory experiments using Nd:YAG lasers (powers from 23.5 mW to 1.2 kW) provide empirical data on dazzling metrics (e.g., 1200 W/m² intensity for temporary blinding, with CMOS recovery in 10-20 ms versus 200-300 ms for CCD) and damage thresholds (FD95 fluence for probabilistic assessment). The role of color filter arrays (CFA) in modulating spectral response is explored, noting how they amplify absorption in visible bands but offer partial protection in infrared. Nonlinear phenomena, such as multi-photon ionization and avalanche breakdown in p-n junctions and dielectrics, are detailed for short-pulse regimes. Practical applications for drone countermeasures are outlined: low-power visible lasers for short-range dazzling (up to 5 m), high-power infrared systems with telescopic optics for damage at 300 m (intensity 1342 W/m²). Atmospheric attenuation (15-20% reduction in fog) and ethical/legal challenges are addressed. Conclusions summarize findings, recommending adaptive optics and hybrid radio-laser systems for enhanced efficacy, while proposing future research on quantum dot sensors and standardized testing protocols. This synthesis advances laser-based UAV neutralization strategies, blending theoretical insights with experimental validation for robust counter-drone technologies.

Abstract Color Fourier single-pixel imaging (FSI) enables efficient spectral and spatial imaging. Here, we propose the Fourier single-pixel imaging scheme with random color filter array (FSI-RCFA). The proposed method employs the random color filter array (RCFA) to modulate Fourier patterns.A three-step phase-shifting technique reconstructs the Fourier spectrum, followed by the RCFA-based demosaicing algorithm to recover color images. Compared to traditional color FSI based on Bayer color filter array schemes(FSI-BCFA),our approach achieves superior separation between chrominance and luminance components in the frequency domain. Simulation results demonstrate that the FSI-RCFA achieves a lower mean squared error (MSE), a higher peak signal-to-noise ratio (PSNR), and superior noise resistance compared to FSI-BCFA, while enabling direct single-channel pixel measurements for targeted applications such as agricultural defect detection.

This paper presents a high-resolution, single-photon LiDAR imaging camera capable of performing simultaneous depth and color estimation. It features a time-gated CMOS single-photon avalanche diode detector array with a large fill-factor, integrated with a metasurface-based plasmonic color filter array for three-dimensional color imaging. A sliding detector gate enables millimeter-scale depth resolution using as few as two binary planes per step, and high-fidelity color reconstruction is demonstrated from as few as two photons per pixel. This compact, photon-efficient system offers depth-sensitive, multispectral imaging possibilities for many diverse applications such as space exploration, autonomous navigation, augmented/virtual reality, and biomedical imaging.

Modern colour image sensors face challenges in further improving sensitivity and image quality because of inherent limitations in light utilization efficiency1. A major factor contributing to these limitations is the use of passive optical filters, which absorb and dissipate a substantial amount of light, thereby reducing the efficiency of light capture2. On the contrary, active optical filtering in Foveon-type vertically stacked architectures still struggles to deliver optimal performance owing to their lack of colour selectivity, making them inefficient for precise colour imaging3. Here we introduce an innovative architecture for colour sensor arrays that uses multilayer monolithically stacked lead halide perovskite thin-film photodetectors. Perovskite bandgap tunability4 is utilized to selectively absorb the visible light spectrum’s red, green and blue regions, eliminating the need for colour filters. External quantum efficiencies of 50%, 47% and 53% are demonstrated for the red, green and blue channels, respectively, as well as a colour accuracy of 3.8% in ΔELab outperforming the state-of-the-art colour-filter array and Foveon-type photosensors. The image sensor design improves light utilization in colour sensors and paves the way for the next generation of highly sensitive, artefact-free images with enhanced colour fidelity.

E Ink has developed a full‐color electrophoretic display platform with excellent color and contrast ratio performance targeting outdoor signage applications. Full color displays utilizing colored particles, no color filter array, and a single TFT array backplane have previously been demonstrated in a microcup structure using E Ink's ACeP technology and black‐and‐white displays are wellestablished using microcapsule structures. This new E Ink platform combines the ACeP pigment color making mechanisms with the microcapsule structure to enable large area displays (up to 75”) and wide operating range (‐20°C to 65°C).

Fabrication process and testing results are reported for reflective color filter arrays incorporating quantum dot ink layers. Approximately 1.35x higher total reflectivity and 2.5x higher on‐axis luminance in respect to conventional absorptive color filters are demonstrated. The components can be used in reflective or transflective liquid crystal or electrowetting displays.

The increasing occurrence of digital images in various domains like social media, journalism, and law enforcement has created a need for secure methods to verify their originality and determine their source. Images can easily be manipulated thereby leading to misinformation and other serious consequences. This opens the gateway for different source camera identification techniques like Photo Response Non-Uniformity (PRNU) analysis, Colour Filter Array (CFA) interpolation, Auto-white balance (AWB) approximation, lens radial distortion, and many more. Factors like image type, JPEG compression level, rotation and gamma correction affect the accuracy and reliability of these methods. Henceforth, this paper aims to systematically review source camera identification techniques, detailing their accuracy and applicability across different datasets and camera models.

DREAM-CFA: joint learning of binary color filter array and demosaicing

Abstract Color filter arrays play a vital role in spectrally discriminating light into red, green and blue (RGB) bands—widely utilized in color cameras and display technology. Existing absorptive dye color filters exhibit low transmission efficiencies and require thicknesses several times that of the lateral pixel sizes they cover in order to achieve sufficient spectral discrimination (leading to cross-talk). Structural color generation based on metasurfaces may alleviate the challenges of dye-based counterparts, due to potential of achieving high pixel resolution within sub-wavelength thicknesses and easily customizable filter functions. However, achieving high efficiency transmission modes for RGB colors (with high saturation) remains challenging. In this work, we present designs for Complementary Metal–Oxide–Semiconductor-compatible high efficiency RGB color filters based on hybrid metal-dielectric metasurfaces composed of aluminum (Al) and titanium dioxide (TiO2) pillars. The interaction between plasmonic and Mie resonances results in hybrid modes with transmission efficiencies of 80% and a full-width-half-maximum ranging between 42 and 67 nm. By optimizing geometric parameters, we achieve highly saturated transmission-mode colors, covering a wide color gamut of the standard RGB color space. The presented filter designs offer a route toward high efficiency color filters for application across multiple technologies including optical imaging, spectral sensing, and mead-mounted displays.

Snapshot spectral imaging has been an active area of research and has attracted significant attention. However, few studies have considered the diversity of light sources, leading to overfitting on specific types of illuminants. In this paper, we propose an illuminant augmentation method to improve the generalization ability. The color filter arrays (CFAs) of RGB cameras can encode wavelength information to colors but meet metamerism. To tackle this issue, we further optimize a diffractive optics element (DOE) for phase coding in an end-to-end manner. We build a phase-wavelength joint coding compressive imaging system that shows better generalization ability than a conventional RGB camera. To enhance the spectral reconstruction performance, we introduce a novel mamba-based network, snapshot spectral compressive imaging mamba (SSCIM). Additionally, we have collected a well-calibrated hyperspectral dataset of relative reflectance for testing. Experimental results demonstrate that our method achieves state-of-the-art performance in hyperspectral reconstruction and illuminant generalization.

The entire Image Signal Processor (ISP) of a camera relies on several processes to transform the data from the Color Filter Array (CFA) sensor, such as demosaicing, denoising, and enhancement. These processes can be executed either by some hardware or via software. In recent years, Deep Learning (DL) has emerged as one solution for some of them or even to replace the entire ISP using a single neural network for the task. In this work, we investigated several recent pieces of research in this area and provide deeper analysis and comparison among them, including results and possible points of improvement for future researchers.

Conventional digital cameras combine absorbing color filter arrays with microlenses to achieve color imaging and improve efficiency. Such cameras require multi-step and multi-material fabrication processes. Several recent efforts have investigated metasurface-based color routing to combine focusing with filtering in a single functional layer with an improved efficiency. These approaches require high-refractive index materials and deep sub-micron fabrication to realize the metasurfaces. We present here an alternative, 2.5 dimensional metasurface that simultaneously provides both color filtering and focusing, but requires only a low-refractive index polymer and micron-scale patterning such that it is suitable for replication by molding. Unlike Bayer filters, this metasurface produces six independent spectra focused on nine monochrome pixels yielding both a high efficiency and low color error. These metasurfaces could be more photo-stable and thermally stable than dye-based filters and less expensive to produce than conventional arrays or metasurface color routers. Here, we characterize a metasurface-based focusing color filter array prototyped using two-photon lithography whose efficiencies are competitive with Bayer filters and whose color error is comparable to the limit of human perception.

Snapshot Hyperspectral Imaging With Co-Designed Optics, Color Filter Array, and Unrolled Network

With the prevalence of emerging computer vision applications, the demand for capturing dynamic scenes with high-speed motion has increased. A kind of neuromorphic sensor called spike camera shows great potential in this aspect since it generates a stream of binary spikes to describe the dynamic light intensity with a very high temporal resolution. Color spike camera (CSC) was recently invented to capture the color information of dynamic scenes via a color filter array (CFA) on the sensor. This paper proposes a long short-term temporal aggregation strategy of spike signals. First, we utilize short-term temporal correlation to adaptively extract temporal features of each time point. Then we align the features and aggregate them to exploit long-term temporal correlation, suppressing undesired motion blur. To implement the strategy, we design a CSC reconstruction network. Based on adaptive short-term temporal aggregation, we propose a spike representation module to extract temporal features of each color channel, leveraging multiple temporal scales. Considering the long-term temporal correlation, we develop an alignment module to align the temporal features. In particular, we perform motion alignment of red and blue channels with the guidance of the higher-sampling-rate green channel, leveraging motion consistency among color channels. Besides, we propose a module to aggregate the aligned temporal features for the restored color image, which exploits color channel correlation. We have also developed a CSC simulator for data generation. Experimental results demonstrate that our method can restore color images with fine texture details, achieving state-of-the-art CSC reconstruction performance.

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.

Joint learning of RGBW color filter arrays and demosaicking

An impartial framework to investigate demosaicking input embedding options

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.