Low-resolution Color Research Articles

Enhanced colorimetric analysis substrate based on graphene oxide open-close actuator

Traditional colorimetric sensors often suffer from low color resolution and insufficient sensitivity. Therefore, for colorimetric detection to serve as a reliable quantitative analysis technique, an enhanced substrate with high sensitivity and repeatability is required. This strategy employs an interfacial network constructed between two-dimensional graphene oxide (GO) lamellar structures to create a substrate for colorimetric analysis. Under normal conditions, the newly synthesized GO inhibits oxidative activity due to van der Waals forces between the lamellar interfaces, resulting in the GO actuator being in the closed state. During Ag+ colorimetric analysis, the GO interlayer adsorption of silver nanoparticles (AgNPs) disrupts the interlayer van der Waals forces, releasing its oxidase activity and significantly enhancing Ag+ oxidation. The resulting actuator exhibits a faster driving speed (0.045 μM/s), excellent sensitivity, and stability. Additionally, the reducing properties of dopamine (DA) can be utilized to encapsulate the GO actuator with polydopamine, closing the GO actuator. Similarly, the reducing property of ascorbic acid (AA) can be utilized to restore the GO actuator to a closed state. These distinct reduction mechanisms allow for an effective visual distinction between AA and DA. With these advantages, the actuator can be developed into simple analytical tools (such as Ag+ colorimetric test strips and DA and AA test kits), demonstrating significant potential for practical application.

Leukocyte detection based on convolutional neural network fusion with color Fourier ptychographic microscopy

Abstract In the field of optical microscopic imaging, color Fourier Ptychographic Microscopy (FPM) technology has attracted much attention due to its advantages of large field of view, high resolution, and quantitative phase imaging. In this paper, a color FPM fusion algorithm based on deep learning is proposed in combination with Convolutional Neural Networks (CNN) and applied to leukocyte detection. Firstly, this paper introduces a fusion model of a convolutional neural network based on the traditional color FPM imaging method and fuses low-resolution color images and high-resolution grayscale images through a multilayer convolutional network. This method improves the quality of reconstructed images while reducing the reconstruction time. Secondly, this paper constructs a leukocyte detection dataset by using an improved color FPM reconstruction algorithm and builds a leukocyte detection system based on the YOLOv7 architecture. This paper shows that combining convolutional neural networks with color FPM technology can provide higher-quality reconstructed images in medical imaging and cell detection, which provides strong technical support for digital pathology and medical diagnosis.

The Development of Various Methods for Object Tracking and Classification in Thermal Videos

Nowadays, thermal cameras play a major role because of its temperature-based photography in many applications such as video surveillance, monitoring electronics/electrical machines, forest monitoring, monitoring babies/adult patients, and suspicious object detection. Tracking pedestrians in thermal video is a major task for such applications. Thermal cameras usually create images based on temperature emitted by the object only and not on the lighting conditions and outdoor environment conditions. But still thermal images have constraints like no texture or colour information, more number of dead pixels, low resolution, and noticeable visual colour patterns in case of any temperature variations. So the challenge in tracking pedestrians in thermal videos is tracking objects/pedestrians throughout the video without an identity switch by overcoming these constraints which may mislead the tracking process. To overcome these constraints, the proposed system uses tri feature matrix (TFM) as an object descriptor which is used to uniquely identify and represent objects in thermal images. TFM is represented in more compact way as a triple matrix. It is a simple and accurate descriptor suitable for tracking objects in thermal video sequences without an identity switch. The proposed Pedestrian tracking system uses most of the advantages of thermal cameras by overcoming challenges in thermal videos effectively based on a novel descriptor TFM. The proposed system is evaluated with various data sets, and the results are analysed using true positive, true negative, false negative, false positive, accuracy, precision, recall, F-score, global identity mismatch (GMME) and track matching error (TME). The performance metrics such as accuracy, precision, recall, F-score, GMME and TME are computed as 99%, 100%, 99%, 99%, 2.3%, and 2.1%, respectively. From the observation, it is found that the performance of proposed TFM-based system is significantly improved. The experimental result shows that the proposed system achieved more accurate tracking compared to the conventional methods

The software for improvement of image quality after enlargement

In the paper digital images of various formats were investigated. The different vector image formats have different color rendering capabilities. The main task was to achieve a result of refinement of the random low resolution color raster image without quality and resolution loss. The biggest advantage of using specific vector or compressed raster formats is the ability of scaling without quality loss and comparatively small file size. This eases vector images transfer through networks. In the article a specific algorithm of raster images refinement was investigated, particularly the method of raster images refinement based on combination of interpolation algorithms with and without square root of the color values. The key point of the method is comparison and combination of vertical, horizontal and diagonal interpolation that allows to achieve better precision on color depth calculation. This exact method was never used in commercial of scientific software though there are different variation of combined interpolation methods similar to current one. In this paper two different approaches to image matrix re-calculation during image refinement were tested, in order to research how root squaring the value of color depth would affect the target color value. The result shows that this approach allows to keep more details in shadows and save contours during interpolation though the images lose somewhat of color depth. The experiment shows that this interpolation method with square rooting color values allows to enlarge and refine color images with complex tone curve structure and keep details of the objects in place, though color depth is worsened especially in deepest shades and blacks. On the opposite the method of combined interpolation without root squaring gives significantly better result with color interpolation but loses details in the dark areas of the initial image. The suggested method can be used in a number of different areas

Target detection algorithm based on super- resolution color remote sensing image reconstruction

An improved generative adversarial network model is adopted to improve the resolution of remote sensing images and the target detection algorithm for color remote sensing images. The main objective is to solve the problem of training super-resolution reconstruction algorithms and missing details in reconstructed images, aiming to achieve high-precision detection of medium and low-resolution color remote sensing targets. First, a lightweight image super-resolution reconstruction algorithm based on an improved generative adversarial network (GAN) is proposed. This algorithm combines the pixel attention mechanism and up-sampling method to restore image details. It further integrates edge-oriented convolution modules into traditional convolution to reduce model parameters and achieve better feature collection. Then, to further enhance the feature collection ability of the model, the YOLOv4 object detection algorithm is also improved. This is achieved by introducing the Focus structure into the backbone feature extraction network and integrating multi-layer separable convolutions to improve the feature extraction ability. The experimental results show that the improved target detection algorithm based on super resolution has a good detection effect on remote sensing image targets. It can effectively improve the detection accuracy of remote sensing images, and have a certain reference significance for the realization of small target detection in remote sensing images.

Low-Light Color Imaging via Cross-Camera Synthesis

This paper presents a framework for low-light color imaging using a dual camera system that combines a high spatial resolution monochromatic (HSR-mono) image and a low spatial resolution color (LSR-color) image. We propose a cross-camera synthesis (CCS) module to learn and transfer illumination, color, and resolution attributes across paired HSR-mono and LSR-color images to recover brightness- and color-adjusted high spatial resolution color (HSR-color) images at both camera views. Jointly characterizing various attributes for final synthesis is extremely challenging because of significant domain gaps across cameras. Our proposed CCS method consists of three subtasks: reference-based illumination enhancement ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefIE</b> ), reference-based appearance transfer ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefAT</b> ), and reference-based super resolution ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefSR</b> ), by which we can characterize, transfer, and enhance illumination, color, and resolution at both views. Each subtask is implemented using deep neural networks (DNNs) that are first trained for each subtask separately and then finetuned jointly. Experimental results show the superior qualitative and quantitative results of the proposed CCS model on both synthetic content from popular datasets and real-captured scenes. Ablation studies further verify the model generalization to various exposures and camera baselines. We will make our work accessible at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://njuvision.github.io/CCS</uri> for reproducible research.

A lightweight convolutional network for infrared object detection and tracking

As an important application of computer vision, visual tracking has being a fundamental topic. Compared with visible image, infrared image has the characteristic of low resolution, blurred contour and single color feature. Thus, it is still a challenge for infrared object tracking. Further, it is difficult to balance the real-time performance and accuracy. This paper proposed a method for target detection and tracking, with a deeper and lightweight MobileNet V2 structure as the backbone network. In the end, the tracker is tested on various datasets. Result shows that the tracker can get a balance between tracking accuracy and inference speed, which is crucial for deployment on mobile devices.

Integrating multiple hybridization chain reactions on gold nanoparticle and alkaline phosphatase-mediated in situ growth of gold nanobipyramids: An ultrasensitive and high color resolution colorimetric method to detect the mecA gene of Staphylococcus aureus

Colorimetry has been considered as a potential instrument-free platform for point-of-care genomic detection. However, it is limited by the poor sensitivity and low color resolution. Herein, we report a high-resolution colorimetric biosensor based on multiple hybridization chain reactions (HCRs) on gold nanoparticle (AuNP) and alkaline phosphatase (ALP)-mediated in situ growth of gold nanobipyramids (AuNBPs) for ultrasensitive detection of the Staphylococcus aureus (S. aureus) mecA gene. In our design, target DNA is hybridized with capture hairpin DNA on magnetic beads and then amplified by multiple HCRs on AuNP. Since biotin-labeled hairpin-structured nucleic acids are utilized to conduct HCRs, together with the large specific surface area of AuNP, the biotin- and streptavidin- based reaction results in a large amount of ALP on AuNP. With the aid of NADPH, ALP-mediated in situ growth of AuNBPs is observed, and a series of rainbow-like colors are associated with different target DNA concentrations. Through the multiple-amplification strategy produced by AuNP, HCRs, and enzymatic reactions, the target DNA as low as 2.71 pM can be detected with high specificity. Moreover, this method has been successfully applied to detect the mecA gene extracted from S. aureus. Therefore, the proposed method holds great potential in clinical diagnosis.

Pedestrian tracking in thermal videos using TFM (tri-feature matrix)

Nowadays, thermal cameras play a major role because of its temperature-based photography in many applications such as video surveillance, monitoring electronics/electrical machines, forest monitoring, monitoring babies/adult patients, and suspicious object detection. Tracking pedestrians in thermal video is a major task for such applications. Thermal cameras usually create images based on temperature emitted by the object only and not on the lighting conditions and outdoor environment conditions. But still thermal images have constraints like no texture or colour information, more number of dead pixels, low resolution, and noticeable visual colour patterns in case of any temperature variations. So the challenge in tracking pedestrians in thermal videos is tracking objects/pedestrians throughout the video without an identity switch by overcoming these constraints which may mislead the tracking process. To overcome these constraints, the proposed system uses tri feature matrix (TFM) as an object descriptor which is used to uniquely identify and represent objects in thermal images. TFM is represented in more compact way as a triple matrix. It is a simple and accurate descriptor suitable for tracking objects in thermal video sequences without an identity switch. The proposed Pedestrian tracking system uses most of the advantages of thermal cameras by overcoming challenges in thermal videos effectively based on a novel descriptor TFM. The proposed system is evaluated with various data sets, and the results are analysed using true positive, true negative, false negative, false positive, accuracy, precision, recall, F-score, global identity mismatch (GMME) and track matching error (TME). The performance metrics such as accuracy, precision, recall, F-score, GMME and TME are computed as 99%, 100%, 99%, 99%, 2.3%, and 2.1%, respectively. From the observation, it is found that the performance of proposed TFM-based system is significantly improved. The experimental result shows that the proposed system achieved more accurate tracking compared to the conventional methods.

Single color image super-resolution using sparse representation and color constraint

Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm (e.g., L 1 or L 2 ). These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images. This paper presents a color super-resolution reconstruction method combining the L 2 / 3 sparse regularization model with color channel constraints. The method converts the low-resolution color image from RGB to YCbCr. The L 2 / 3 sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image. Then the color channel-constraint method is adopted to remove artifacts of the reconstructed high-resolution image. The method not only ensures the reconstruction quality of the color image details, but also improves the removal ability of color artifacts. The experimental results on natural images validate that our method has improved both subjective and objective evaluation.

Simply converting color signal readout into thermal signal readout for breaking the color resolution limitation of colorimetric sensor

Traditional colorimetric sensors often suffer from the low color resolution and the not well-defined boundary between the yes and no answers, especially for distinguishing the similar color readouts with the naked eye. Herein, a simple strategy is communicated for greenly converting colorimetric sensor into a photothermal sensor, which is based on the simple color-to-thermal (temperature) signal readout conversion. The colorimetric system based on simply mixing commercially available chemicals of 3,3,5,5-tetramethylbenzidine (TMB) and silver ions (Ag+), provides a straightforward blue color readout owing to the generation of oxidized TMB (TMBox). In the presence of the target alkaline phosphatase (ALP), ascorbic acid phosphate (AAP) in the system could be hydrolyzed into ascorbic acid (AA), subsequently reducing the blue colored TMBox into colorless TMB. The blue-to-colorless change-based colorimetric system also exhibits the highly sensitive near-infrared (NIR) laser-driven photothermal response, allowing the successful thermometer-detectable signal-based assay for point-of-care testing (POCT) of ALP levels just using a common thermometer. This study not only demonstrated a regulatory strategy to convert color-based signal readout into thermal (temperature)-based signal readout, but also established a facile method to design affordable thermometric sensors for POCT and instrument-free bioanalysis, especially in low-resource settings.

Target-mediated surface chemistry of gold nanorods for breaking the low color resolution limitation of monocolorimetric sensor

A long-term limitation of low color resolution in the monocolorimetric sensors seriously restricts its extending application, especially for the traditional organic dyes-based monocolorimetric sensors. Herein, we explore a simple target-mediated surface chemistry-based modulation for regulating the surface of gold nanorods (AuNRs), resulting in a successful multicolorimetric sensor for inorganic pyrophosphatase (PPase) activity. It operates on the principle that PPase modulates the release of Fe3+ free from the ferric-pyrophosphate complex (Fe3+-PPi) through a specific target-catalyzed hydrolysis process, leading to a better mediation for the unique chemical redox reaction between Fe3+ and I−. Subsequently, I− is oxidized into I2, which as a moderate oxidant further regulates the surface of AuNRs due to the rapidly etching reaction between I2 and AuNRs. As a result, the resultant AuNRs surface change further leads to a blue-shift longitudinal LSPR effect of AuNRs, along with a naked-eye-detectable rainbow-like multicolor signal readout. Under optimal conditions, the proposed multicolorimetric sensor exhibits an affordable sensing performance for PPase activity in the range from 1.5 U L−1 to 9 U L−1 with a detection limit of 0.8 U L−1 (S/N = 3). To this point, our strategy not only provides a promising way for breaking the low color resolution limitation of the traditional monocolorimetric sensors, but also broadens the applicability of AuNRs etching-based multicolorimetric sensors.

Multi-Frame RGB/NIR Imaging for Low-Light Color Image Super-Resolution

We propose a method for super-resolution (SR) of low-resolution (LR) color images taken in low-light scenes. Our method is based on the multi-frame SR technique, which reconstructs a high-resolution color image by fusing multiple LR images that were taken at different camera positions so as to alter the alignment between scene details and pixels. Previous multi-frame SR methods have implicitly assumed that the LR images could be captured with less noise and blur. However, images taken in low-light scene will contain high level of noise and motion blur, making it difficult to achieve high quality SR. To overcome these problems, we utilize a single sensor that captures red, green, blue (RGB) and near-infrared (NIR) information. We capture multiple raw images of a low-light scene using our RGB/NIR single sensor with an NIR flash unit. Because a NIR flash can provide sufficient illumination for low-light scenes, the structural scene information that, contributes to an effective SR process, can be captured with high quality. With the help of the NIR information, we jointly perform deblurring, denoising and SR of LR RGB/NIR raw images. Our experiments demonstrate the effectiveness of the proposed method using synthetic and real RGB/NIR raw images.

Image restoration and color fusion of digital microscopes.

Traditional microscopes do not meet a wide field of view and high resolution at the same time. We propose a method for image restoration and color fusion of digital microscopes. It combines a single high-resolution gray scale hologram with a low-resolution color image to obtain a high-resolution color image. Specifically, the high-resolution gray scale image is obtained by reconstructing three different height holograms using a wavelet-based method, and the color information is obtained using a portable cell phone microscope. The subsequent calibration and blending of colors ultimately results in a high-resolution, wide-field color map that can be of great help in the study of pathology or biomedicine. This method breaks the rule that the large field of view and high resolution of a traditional microscope cannot be simultaneously satisfied, which can realize a more comprehensive observation of the shape and details of the slice.

Synthetic aperture radar and optical remote sensing image fusion for flood monitoring in the Vietnam lower Mekong basin: a prototype application for the Vietnam Open Data Cube

ABSTRACT Flood monitoring systems are crucial for flood management and consequence mitigation in flood prone regions. Different remote sensing techniques are increasingly used for this purpose. However, the different approaches suffer various limitations, including cloud and weather effects (optical data), and low spatial resolution and poor colour presentation (synthetic aperture radar data). This study fuses two data types (Landsat and Sentinel-1) to overcome these limitations and produce better quality images for a prototype flood application in the Vietnam Open Data Cube (VODC). Visual and quantitative evaluation of fused image quality revealed improvement in the images compared with the original scenes. Ground-truth data was used to develop the study flood extraction algorithm and we found a good agreement between our results and SERVIR Mekong (a joint initiative by the US agency for International Development (USAID), National Aeronautics and Space Administration (NASA), Myanmar, Thailand, Cambodia, Laos and Vietnam) maps. While the algorithm is run on a personal computer (PC), it has a clear potential to be developed for application on a big data system.

Simultaneous color-depth super-resolution with conditional generative adversarial networks

In this paper, color-depth conditional generative adversarial networks (CDcGAN) are proposed to resolve the problems of simultaneous color image super-resolution and depth image super-resolution in 3D videos. Firstly, a generative network is presented to leverage the mutual information of the low-resolution color image and low-resolution depth image so that they can enhance each other considering their geometric structural similarity in the same scene. Secondly, three auxiliary losses of data loss, total variation loss, and 8-connected gradient difference loss are introduced to train this generative network to ensure that the generated images are close to the real ones in addition to the adversarial loss. Finally, we study the CDcGAN and its variants. Experimental results show that the proposed approach can produce the high-quality color image and depth image from a pair of low-quality images, and it is superior to several other leading methods. Additionally, it has also been used to resolve the problems of concurrent image smoothing and edge detection, as well as the problem of HR-color-image-guided depth super-resolution to show the effectiveness and universality of the proposed method.

Depth image super-resolution based on joint sparse coding

This paper proposes a new approach to single depth image super-resolution (SR), based upon a novel joint sparse coding model. A low-resolution color is used as a guide in the SR process. Firstly, we introduce synthetic characteristic image patch to learn a joint dictionary from the low-resolution depth map as well as its corresponding low-resolution intensity image. Then, we derive the joint nonlocal center sparse representation model based on sparse coding and theoretical analysis. In reconstruction process, we use Bayesian interpretation approach to estimation the sparse code coefficients for each unknown HR image patch. Meanwhile, we use an iterative algorithm to solve the JSC model. In addition, we exploit image patch redundancy within and across different scales, produce visually pleasing results without extensive training on external database. Experimental results demonstrate that the proposed method outperforms favorably many current state-of-the-art depth map super-resolution approaches on both visual effects and objective image quality and underpin the validity of our proposed model.

A High-Efficiency Single-Inductor Multiple-Output Buck-Type LED Driver With Average Current Correction Technique

The state-of-the-art topology of a single-inductor multiple-output light emission diode (LED) driver employs time-multiplexing (TM) control multiple parallel LED strings. The TM control technology provides large output current but the inductor current is separated to several parts and unable to be used simultaneously. The limitation of TM control also results in low dimming frequency and low color resolution. Thus, a single-inductor multiple-floating-output (SIMFO) LED driver with the average current correction (ACC) technique is proposed to solve these problems, so the inductor current is simultaneously utilized by every LED in series, with enhancement of luminous efficiency. Furthermore, the proposed ACC technique adaptively adjusts the average current to eliminate the current cross-regulation effect occurring in different dimming patterns, so each different color LED has its own dimming ratio. The test chip is fabricated in a 0.5-μm high voltage (HV) process with an active area of 15 mm2 , and experimental results show the proposed LED driver achieves 24-b color resolution with 12 W output power and maintains 96% high efficiency in the RGBW LED applications.

DeepGrapes: Precise Detection of Grapes in Low-resolution Images

We proposed a deep convolutional network aimed at detection of single grapes of white varieties in low-resolution colour images. With accuracy, precision and recall at 97.35 %, 96.53 %, and 98.27 %, the proposed grape detector is a state of the art solution. The detector is intended for practical applications that require high exactitude, such as in the process of yield estimation.

Mixed-primary factorization for dual-frame computational displays

Increasing resolution and dynamic range of digital color displays is challenging with designs confined by cost and power specifications. This necessitates modern displays to trade-off spatial and temporal resolution for color reproduction capability. In this work we explore the idea of joint hardware and algorithm design to balance such trade-offs. We introduce a system that uses content-adaptive and compressive factorizations to reproduce colors. Each target frame is factorized into two products of high-resolution monochromatic and low-resolution color images, which then get integrated through temporal or spatial multiplexing. As our framework minimizes the error in colorimetric space, the perceived color rendition is high, and thanks to GPU acceleration, the results are generated in real-time. We evaluate our system with a LCD prototype that uses LED backlight array and temporal multiplexing to reproduce color images. Our approach enables high effective resolution and dynamic range without increasing power consumption. We also demonstrate low-cost extensions to hyperspectral and light-field imaging, which are possible due to compressive nature of our system.