Daytime Color Research Articles

Comparing hybrid models for recognising objects in thermal images at nighttime

This research aims to revolutionize urban object recognition by developing cloud-based Python programs using intelligent algorithms. Unlike current models that focus on colour enhancement in nighttime thermal images, this work addresses the critical challenge of accurate object detection in urban landscapes. The proposed method incorporates a binary generative adversarial network (GAN) generator that can switch bidirectionally between daytime colour (DC) and nighttime infrared (NTIR) images. memory-based visual image memory (MVAM), system extracts important descriptive information from urban landscape images, reducing problems related to small sample sizes. This discussion presents a comprehensive improvement and evaluation of a deep learning image classification pipeline using Google Colab, demonstrating advanced image processing. Using TensorFlow, Keres and scikit image libraries combined with advanced algorithms such as DenseNet121 and MobileNetV2 presents a clear approach. We created a Bidirectional GAN + MVAM for object recognition in this work. Our method performed well, with an accuracy of 81.43%, precision of 51.16, recall of 50.11, and F-score of 46.37. The systematic presentation of the code presents a careful strategy to ensure optimal performance, stability, and efficiency of deep learning and image processing tasks.

Nighttime Thermal Infrared Image Translation Integrating Visible Images

Nighttime Thermal InfraRed (NTIR) image colorization, also known as the translation of NTIR images into Daytime Color Visible (DCV) images, can facilitate human and intelligent system perception of nighttime scenes under weak lighting conditions. End-to-end neural networks have been used to learn the mapping relationship between temperature and color domains, and translate NTIR images with one channel into DCV images with three channels. However, this mapping relationship is an ill-posed problem with multiple solutions without constraints, resulting in blurred edges, color disorder, and semantic errors. To solve this problem, an NTIR2DCV method that includes two steps is proposed: firstly, fuse Nighttime Color Visible (NCV) images with NTIR images based on an Illumination-Aware, Multilevel Decomposition Latent Low-Rank Representation (IA-MDLatLRR) method, which considers the differences in illumination conditions during image fusion and adjusts the fusion strategy of MDLatLRR accordingly to suppress the adverse effects of nighttime lights; secondly, translate the Nighttime Fused (NF) image to DCV image based on HyperDimensional Computing Generative Adversarial Network (HDC-GAN), which ensures feature-level semantic consistency between the source image (NF image) and the translated image (DCV image) without creating semantic label maps. Extensive comparative experiments and the evaluation metrics values show that the proposed algorithms perform better than other State-Of-The-Art (SOTA) image fusion and translation methods, such as FID and KID, which decreased by 14.1 and 18.9, respectively.

Thermal Infrared Image Colorization for Nighttime Driving Scenes With Top-Down Guided Attention

Benefitting from insensitivity to light and high penetration of foggy environments, infrared cameras are widely used for sensing in nighttime traffic scenes. However, the low contrast and lack of chromaticity of thermal infrared (TIR) images hinder the human interpretation and portability of high-level computer vision algorithms. Colorization to translate a nighttime TIR image into a daytime color (NTIR2DC) image may be a promising way to facilitate nighttime scene perception. Despite recent impressive advances in image translation, semantic encoding entanglement and geometric distortion in the NTIR2DC task remain under-addressed. Hence, we propose a toP-down attEntion And gRadient aLignment based generative adversarial network, referred to as PearlGAN. A top-down guided attention module and an elaborate attentional loss are first designed to reduce the semantic encoding ambiguity during translation. Then, a structured gradient alignment loss is introduced to encourage edge consistency between the translated and input images. In addition, pixel-level annotation is carried out on a subset of FLIR and KAIST datasets to evaluate the semantic preservation performance of multiple translation methods. Furthermore, a new metric is devised to evaluate the geometric consistency in the translation process. Extensive experiments demonstrate the superiority of the proposed PearlGAN over other image translation methods for the NTIR2DC task. The source code and labeled segmentation masks will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/FuyaLuo/PearlGAN/</uri> .

Daytime colour preference in Drosophila depends on the circadian clock and TRP channels.

Light discrimination according to colour can confer survival advantages by guiding animals towards food and shelter and away from potentially harmful situations1,2. Such colour-dependent behaviour can be learned or innate. Data on innate colour preference in mammals remain controversial3 and there are limited data for simpler organisms4-7. Here we show that, when given a choice among blue, green and dim light, fruit flies exhibit an unexpectedly complex pattern of colour preference that changes according to the time of day. Flies show a strong preference for green in the early morning and late afternoon, a reduced green preference at midday and a robust avoidance of blue throughout the day. Genetic manipulations reveal that the peaks in green preference require rhodopsin-based visual photoreceptors and are controlled by the circadian clock. The midday reduction in green preference in favour of dim light depends on the transient receptor potential (TRP) channels dTRPA1 and Pyrexia, and is also timed by the clock. By contrast, avoidance of blue light is primarily mediated by multidendritic neurons, requires rhodopsin 7 and the TRP channel Painless, and is independent of the clock. Our findings show that several TRP channels are involved in colour-driven behaviour in Drosophila, and reveal distinct pathways of innate colour preference that coordinate the behavioural dynamics of flies in ambient light.

Improved Color Mapping Methods for Multiband Nighttime Image Fusion

Previously, we presented two color mapping methods for the application of daytime colors to fused nighttime (e.g., intensified and longwave infrared or thermal (LWIR)) imagery. These mappings not only impart a natural daylight color appearance to multiband nighttime images but also enhance their contrast and the visibility of otherwise obscured details. As a result, it has been shown that these colorizing methods lead to an increased ease of interpretation, better discrimination and identification of materials, faster reaction times and ultimately improved situational awareness. A crucial step in the proposed coloring process is the choice of a suitable color mapping scheme. When both daytime color images and multiband sensor images of the same scene are available, the color mapping can be derived from matching image samples (i.e., by relating color values to sensor output signal intensities in a sample-based approach). When no exact matching reference images are available, the color transformation can be derived from the first-order statistical properties of the reference image and the multiband sensor image. In the current study, we investigated new color fusion schemes that combine the advantages of both methods (i.e., the efficiency and color constancy of the sample-based method with the ability of the statistical method to use the image of a different but somewhat similar scene as a reference image), using the correspondence between multiband sensor values and daytime colors (sample-based method) in a smooth transformation (statistical method). We designed and evaluated three new fusion schemes that focus on (i) a closer match with the daytime luminances; (ii) an improved saliency of hot targets; and (iii) an improved discriminability of materials. We performed both qualitative and quantitative analyses to assess the weak and strong points of all methods.

Impacts of the retinal environment and photoreceptor type on functional regeneration.

Retinal regeneration is a promising central nervous system (CNS) target amongst the various stem cell therapy pursuits, due to its accessibility for manipulation and its disposition towards longitudinal monitoring of treatment safety and efficacy. We offer our perspective on current hurdles towards functional regeneration of cone photoreceptors. Cones are key: For patients suffering vision loss, cone photoreceptors are a required cellular component to restoring daytime vision, colour vision, and high acuity vision. The challenges of regenerating cones contrast with logistic challenges of regenerating rod photoreceptors, which underlines the importance of evaluating context in degeneration and regeneration studies. Foundational research is required to delineate the factors required to generate a diversity of cones in the human macula, and to coax both remaining and newly regenerating cones to rewire towards restoring daytime colour vision. A complex interplay between cell-intrinsic factors and the retinal environment determine both the specification of cone fates and the synaptic plasticity enabling their functional integration. Recent revelations that cellular materials are transferred amongst photoreceptor progenitors further emphasize the critical role of neighbouring cells in directing stem cell fates. From our vantage point, translation of stem cell therapies to restore the cone-rich human macula must be borne upon foundational research in cone-rich retinas. Research frameworks centered on patient outcomes should prioritize animal models and functional outputs that enable and report functional restoration of cone-mediated vision.

A G Protein-Coupled Receptor Dimerization Interface in Human Cone Opsins.

G protein-coupled receptors (GPCRs) detect a wide variety of physical and chemical signals and transmit that information across the cellular plasma membrane. Dimerization is a proposed modulator of GPCR signaling, but the structure and stability of class A GPCR dimerization have been difficult to establish. Here we investigated the dimerization affinity and binding interface of human cone opsins, which initiate and sustain daytime color vision. Using a time-resolved fluorescence approach, we found that human red cone opsin exhibits a strong propensity for dimerization, whereas the green and blue cone opsins do not. Through mutagenesis experiments, we identified a dimerization interface in the fifth transmembrane helix of human red cone opsin involving amino acids I230, A233, and M236. Insights into this dimerization interface of red cone opsin should aid ongoing investigations of the structure and function of GPCR quaternary interactions in cell signaling. Finally, we demonstrated that the same residues needed for dimerization are also partially responsible for the spectral tuning of red cone opsin. This last observation has the potential to open up new lines of inquiry regarding the functional role of dimerization for red cone opsin.

Thermal image colorization using Markov decision processes

Displaying night-vision thermal images with day-time colors is paramount for scene interpretation and target tracking. In this paper, we employ object recognition methods for colorization, which amounts to segmenting thermal images into plants, buildings, sky, water, roads and others, then calculating colors to each class. The main thrust of our work is the introduction of Markov decision processes (MDP) to deal with the computational complexity of the colorization problem. MDP provides us with the approaches of neighborhood analysis and probabilistic classification which we exploit to efficiently solve chromatic estimation. We initially label the segments with a classifier, paving the way for the neighborhood analysis. We then update classification confidences of each class by MDP under the consideration of neighboring consistency and scenery layout. Finally we calculate the colors for every segment by blending the characteristic colors of each class it belongs to in a probabilistic way. Experimental results show that the colorized appearance of our algorithm is satisfactory and harmonious; the computational speed is quite fast as well.

Fast colorization for single-band thermal video sequences

The color night-vision technology is advantageous to scene interpretation by displaying original monochrome night-vision imagery with colors, leading the future trend of night-vision development. Until recently, the methods of color night-vision are mostly based on multi-band sensor fusion that do not apply to thermal systems which are only sensitive to one spectral band. To address this, we present a novel technique in this paper aiming to directly give thermal videos day-time color appearance. We observe that the interframe pixel׳s luminance changing over thermal videos approximately follows a Double Lomax distribution. Based on this observation and computational reasons, we propose a star-shape mask for pixel matching. We further show how the mask can be used for efficient thermal video colorization. Additionally, considering the accumulating errors in sequence, we propose a simple region-similarity based abnormal color correction mechanism. Experiments on real applied thermal night-vision videos show that our method can efficiently render single-band thermal sequence with correct colors at 12–33 frame-per-second (fps), providing observers with enhanced facility of surrounding recognition and target recognition.

A Novel Color Image Fusion for Multi Sensor Night Vision Images

In this paper presents a simple and fast color fusion approach for night vision images. Image fusion involves merging of two or more images in such a way, to get the most advantageous characteristics of each image. Here the Visible image is fused with the InfraRed (IR) image, so the desired result will be single, highly informative image providing full information. This paper focuses on color constancy and color contrast problem. Firstly the contrast of the infrared and visible image is enhanced using Local Histogram Equation. Then the two enhanced images are fused in three compounds of a LAB image using aDWT image fusion. This paper adopts an approach which transfer color from the reference image to the fused image using Color Transfer Technology. To enhance the contrast between the target and the background, a scaling factor is introduced in the transferring equation in the b channel. Finally our approach gives the Multiband Fused image with the natural day-time color appearance and the hot targets are popped out with intense colors while the background details present with the natural color appearance.

Fast color contrast enhancement method for color night vision

The increasing availability and deployment of imaging sensors operating in multiple spectral bands has led to a large research effort in color image fusion, resulting in a plethora of pixel-level image fusion algorithms. In this study a simple and fast fusion approach for color night vision is presented. The contrast of infrared and visible images is adjusted by local histogram equalization. Then the two enhanced images are fused into the three components of a Lab image in terms of a simple linear fusion strategy. To obtain false color images possessing a natural day-time color appearance, this paper adopts an approach which transfers color from the reference to the fused images in a simplified Lab space. To enhance the contrast between the target and the background, a stretch factor is introduced into the transferring equation in the b channel. Experimental results based on three different data sets show that the hot targets are popped out with intense colors while the background details present natural color appearance. Target detection experiments also show that the presented method has a better performance than the former methods, owing to the target recognition area, detection rate, color distance and running time.

One color contrast enhanced infrared and visible image fusion method

Color constancy (Toet and Franken, 2003 [2]; Toet, 2003 [7]) and color contrast (Scribner et al., 2000 [21]; Lee et al., 2005 [23]) are two important topics for color image fusion. The paper focuses on the low color contrast problem of linear fusion algorithms with color transfer method. Color transfer technology is popular in infrared (IR) and visible image fusion to give the fused image a natural day-time color appearance (Toet, 2003 [7]; Wang et al., 2007 [8]; Zheng and Essock, 2008 [9]). However, in the color transfer step, all three channels of the color space are processed with the same linear mapping without color enhancement, resulting in low color contrast between the target and the background (Wang et al., 2007 [8]). Based on the characteristics of the IR image, we introduce a ratio of local to global divergence of the IR image to improve the color contrast. The enhancement ratios for both hot and cold targets are larger than one, while it tends to one for the background. As a result, the proposed method pops out both hot and cold targets in color, where hot targets will appear intense red, and cold targets will appear cyan. Subjective results show visible color contrast enhancement effects. Target detection experiments through hue and saturation components of the fused image show an improvement in the hit rate for target detection, owing to larger color distance between the target and the background.

Fast natural color mapping for night-time imagery

We present a new method to render multi-band night-time imagery (images from sensors whose sensitive range does not necessarily coincide with the visual part of the electromagnetic spectrum, e.g. image intensifiers, thermal camera’s) in natural daytime colors. The color mapping is derived from the combination of a multi-band image and a corresponding natural color daytime reference image. The mapping optimizes the match between the multi-band image and the reference image, and yields a nightvision image with a natural daytime color appearance. The lookup-table based mapping procedure is extremely simple and fast and provides object color constancy. Once it has been derived the color mapping can be deployed in real-time to different multi-band image sequences of similar scenes. Displaying night-time imagery in natural colors may help human observers to process this type of imagery faster and better, thereby improving situational awareness and reducing detection and recognition times.

Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images

Abstract A discussion is presented of daytime sky imaging and techniques that may be applied to the analysis of full-color sky images to infer cloud macrophysical properties. Descriptions of two different types of sky-imaging systems developed by the authors are presented, one of which has been developed into a commercially available instrument. Retrievals of fractional sky cover from automated processing methods are compared to human retrievals, both from direct observations and visual analyses of sky images. Although some uncertainty exists in fractional sky cover retrievals from sky images, this uncertainty is no greater than that attached to human observations for the commercially available sky-imager retrievals. Thus, the application of automatic digital image processing techniques on sky images is a useful method to complement, or even replace, traditional human observations of sky cover and, potentially, cloud type. Additionally, the possibilities for inferring other cloud parameters such as cloud brokenness and solar obstruction further enhance the usefulness of sky imagers.

Fusion of visible and infrared imagery for night color vision

A combined approach for fusing night-time infrared with visible imagery is presented in this paper. Night color vision is thus accomplished and the final scene has a natural day-time color appearance. Fusion is based either on non-negative matrix factorization or on a transformation that takes into consideration perceptual attributes. The final obtained color images possess a natural day-time color appearance due to the application of a color transfer technique. In this way inappropriate color mappings are avoided and the overall discrimination capabilities are enhanced. Two different data sets are employed and the experimental results establish the overall method as being efficient, compact and perceptually meaningful.

Colorizing single band intensified nightvision images

We present a method to give single band intensified nightvision imagery a natural day-time color appearance. For input, the method requires a true color RGB source image and a grayscale nightvision target image. The source and target image are both transformed into a perceptually decorrelated color space. In this color space a best matching source pixel is determined for each pixel of the target image. The matching criterion uses the first order statistics of the luminance distribution in a small window around the source and target pixels. Once a best matching source pixel is found, its chromaticity values are assigned (transferred) to the target pixel while the original luminance value of the target pixel is retained. The only requirement of the method is that the compositions of the source and target scenes resemble each other.

Natural colour mapping for multiband nightvision imagery

We present a method to give (fused) multiband night-time imagery a natural day-time colour appearance. For input, the method requires a false colour RGB image that is produced by mapping three individual bands (or the first three principal components) of a multiband nightvision system to the respective channels of an RGB image. The false colour RGB nightvision image is transformed into a perceptually decorrelated colour space. In this colour space the first order statistics of a natural colour image (target scene) are transferred to the multiband nightvision image (source scene). To obtain a natural colour representation of the multiband night-time imagery, the compositions of the source and target scenes should resemble each other to some degree. The inverse transformation to RGB space yields a nightvision image with a day-time colour appearance. The luminance contrast of the resulting colour image can be enhanced by replacing its luminance component by a grayscale fused representation of the three input bands.

Twilight and daytime colors of the clear sky

Digital image analysis of the cloudless sky's daytime and twilight chromaticities challenges some existing ideas about sky colors. First, although the observed colors of the clear daytime sky do lie near the blackbody locus, their meridional chromaticity curves may resemble it very little. Second, analyses of twilight colors show that their meridional chromaticity curves vary greatly, with some surprising consequences for their calorimetric gamuts.

Daytime colorimetry of retroreflective materials

AbstractThe daytime color of retroreflective materials is a mixture of sensations caused by diffusely reflected light plus a small amount of retroreflected light, the exact mixture depending on the illumination geometry and the goniophotometric characteristics of the material. The most nearly comparable results of color measurement can be obtained with instruments having 45°/0° geometry instead of sphere geometry. Spectrophotometers or colorimeters can be used. The calibration of these instruments is discussed. Most specifications for the daytime color of retroreflective materials call for the use of CIE standard illuminant D65. However, one can use colorimeters designed to give color coordinates for illuminant C provided that color standards calibrated for D65 are used.