Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

Photoprotective Ability of Sunscreens against Ultraviolet, Visible Light and Near-Infrared Radiation

Despite the widespread prevalence of daily sunscreen usage, solar-induced skin damage continues to occur. We have previously reported that solar visible light and near-infrared, in addition to ultraviolet radiation, perform as aging factors and induce deleterious effects such as photoaging, vasodilation, muscle thinning, skin ptosis, photoimmunosupression and photocarcinogenesis. Despite this, most commonly used sunscreens only block ultraviolet radiation. To evaluate the complete solar-spectrum blocking ability of sunscreens produced by internationally well-known companies, a double-beam spectrophotometer was used to optically measure the transmission spectra. The spectrophotometer utilizes a unique, single monochromatic design covering a wavelength range of 240 to 2600 nm. Sunscreens (thickness, 0.1 mm, SPF50+, PA+++ or ++++) from internationally well-known companies blocked 78.8% - 99.9% of ultraviolet, 33.4% - 99.6% of visible light, and 27.0% - 76.4% of near-infrared. It can be concluded that while most commercially available sunscreens filter ultraviolet radiation, they are not effective at blocking visible light and near-infrared radiation. The results of this study imply that sunscreens that provide comprehensive photoprotection from ultraviolet through to near-infrared should be considered to prevent skin photodamage.

Photoprotective Ability of Colored Iron Oxides in Tinted Sunscreens against Ultraviolet, Visible Light and Near-Infrared Radiation

Solar-induced skin damage continues to pose a problem to human health worldwide, despite the widespread recommendation and use of sunscreens. We have previously reported that solar visible light and near-infrared also contribute to skin damage and photoageing. Most commonly recommended sunscreens are only effective throughout the UV spectrum, offering no protection from visible light and near-infrared. To evaluate the enhanced solar-spectrum blocking ability of iron oxides, a double-beam spectrophotometer was used to optically measure the transmission spectra. The spectrophotometer deploys a unique, single monochromatic design to detect wavelength penetration in the range of 240 to 2600 nm. The sample without iron oxide (control) blocked over 80% of ultraviolet-C and ultraviolet-B but did not block ultraviolet-A, visible light, or near-infrared wavelengths. The samples with yellow, and red iron oxide blocked over 90% ultraviolet, but did not block visible light and near-infrared effectively. The sample with black iron oxide blocked visible light, and near-infrared effectively compared with other samples with yellow, blue, and red iron oxide. The sample with red and black iron oxides, and the sample with yellow, blue, red, and black iron oxides blocked ultraviolet through to near-infrared. It can be concluded that dark colored iron oxide combinations are effective at blocking from ultraviolet through to visible light and near-infrared radiation. The results of this study may also suggest that biological colour of human skin and subcutaneous tissues are conserved for comprehensive photoprotection.

Novel Low Viscosity Zinc Oxide, Iron Oxides and Erioglaucine Sunscreen Potential to Protect from Ultraviolet, Visible Light and Near-Infrared Radiation

Despite the widespread recommendation and use of sunscreens and ultraviolet blocking materials, solar-induced skin damage and photoageing continues to pose a problem to human health worldwide. We have previously reported that solar visible light and near-infrared also contribute to skin damage and photo ageing. Most commonly recommended sunscreens are only effective throughout the UV spectrum, offering no protection from visible light and near-infrared. A possible solution could be to augment sunscreens with metal oxides which block visible light and near-infrared radiation. To evaluate the enhanced solar-spectrum blocking ability of novel low viscosity sunscreen containing zinc and iron oxides, a double-beam spectrophotometer was used to optically measure the transmission spectra. The spectrophotometer deploys a unique, single monochromatic design to detect wavelength penetration in the range of 240 to 2600 nm. The Sunscreen base without zinc oxide and iron oxides (control) blocked over 80% of ultraviolet-C and ultraviolet-B but did not block ultraviolet-A, visible light, or near-infrared. The novel low viscosity zinc oxide sample blocked almost over 90% ultraviolet, but did not block visible light and near-infrared sufficiently. However, the samples with the novel low viscosity zinc oxide, iron oxides and erioglaucine blocked almost over 90% of ultraviolet, visible light and near-infrared. It can be concluded that this novel combination of low viscosity zinc oxide, iron oxides and erioglaucine is effective at blocking ultraviolet, visible light and near-infrared radiation. The results of this study imply that sunscreens that provide comprehensive photoprotection from ultraviolet through to near-infrared should be adopted to prevent skin photodamage.

Matching Performance among Visible and near Infrared Coating,Low Infrared Emitting Coating and Microwave Absorbing Coating

The matching performance among the visible and near infrared coating, the low infrared emitting coating and the microwave absorbing coating was investigated.Experimental results show that the resulting material is characteristic of wideband effect ranging from the visible, near infrared and 3-5 μm, 8-14 μm infrared portion of the spectrum, as well as the radar region from 8 to 18 GHz when these three materials form a layer-structure material system.The microwave absorbing ability of material is hardly changed. The resonance peak moves towards lower frequency as the thickness of the visible, near infrared coating and the low infrared emitting coating increases. This problem can be resolved by controlling the thickness of the material. On the other hand, the infrared emissivity ε of the material system increases as the thickness of the visible, near infrared coating increases. This can be resolved by increasing infrared transparency of the visible and near infrared topcoating or controlling its thickness.The experimental resulting material system has spectral reflection characteristics in visible and near infrared regions that are similar to those of the natural background.

Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

The moving window bis corelation coefficients(MW BiCC)was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and B-thalassemia with visible and near-infrared(Vis NIR)spectroscopy.The well-performed moving window principal component analysis linear discriminant analysis(MWPCA-LDA)was also conducted for comparison.A total of 306 transgenic(positive)and 150 nont ransgenic(negative)leave samples of sugarcane were collected and divided to calibration,prediction,and validation.The diffuse reflection spectra were corected using Savitzky-Golay(SG)smoothing with first-order derivative(d=1),third-degree polynomial(p=3)and 25 smpothing points(m=25).The selected waveband was 736-1054nm with MW-BiCC,and the positive and negative validation recognition rates(V_REC^(+),VREC^(-))were 100%,98.0%,which achieved the same effect as MWPCA-LDA.Another example,the 93 B-thalassemia(positive)and 148 nonthalassemia(negative)of human hemolytic samples were colloctod.The transmission spectra were corrected using SG smoothing withd=1,p=3 and m=53.Using M W-BiCC,many best wavebands were selected(e.g.,1116-1146,17941848 and 22842342nm).The V_REC^(+)and V_REC^(-)were both 100%,which achieved the same effect as MW-PCA-LDA.Importantly,the BICC only required ca lculating correlation cofficients between the spectrum of prediction sample and the average spectra of two types of calibration samples.Thus,BiCC was very simple in algorithm,and expected to obtain more applications.The results first confirmed the feasibility of distinguishing B-thalassemia and normal control samples by NIR spectroscopy,and provided a promising simple tool for large population thalassemia screening.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Brand Identification of Wine

High-end wine brand is made through the use of high-quality grape variety and yeast strain, and through a unique process. Not only is it rich in nutrients, but also it has a unique taste and a fragrant scent. Brand identification of wine is difficult and complex because of high similarity. In this paper, visible and near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was used to explore the feasibility of wine brand identification. Chilean Aoyo wine (2016 vintage) was selected as the identification brand (negative, 100 samples), and various other brands of wine were used as interference brands (positive, 373 samples). Samples of each type were randomly divided into the calibration, prediction and validation sets. For comparison, the PLS-DA models were established in three independent and two complex wavebands of visible (400 - 780 nm), short-NIR (780 - 1100 nm), long-NIR (1100 - 2498 nm), whole NIR (780 - 2498 nm) and whole scanning (400 - 2498 nm). In independent validation, the five models all achieved good discriminant effects. Among them, the visible region model achieved the best effect. The recognition-accuracy rates in validation of negative, positive and total samples achieved 100%, 95.6% and 97.5%, respectively. The results indicated the feasibility of wine brand identification with Vis-NIR spectroscopy.

Visible and Near-Infrared Spectroscopy with Multi-Parameters Optimization of Savitzky-Golay Smoothing Applied to Rapid Analysis of Soil Cr Content of Pearl River Delta

Using visible and near-infrared (Vis-NIR) spectroscopy combined with partial least squares (PLS) regression, the rapid reagent-free analysis model for chromium (Cr) content in tideland reclamation soil in the Pearl River Delta, China was established. Based on Savitzky-Golay (SG) smoothing and PLS regression, a multi-parameters optimization platform (SG-PLS) covering 264 modes was constructed to select the appropriately spectral preprocessing mode. The optimal SG-PLS model was determined according to the prediction effect. The selected optimal parameters d, p, m and LV were 2, 6, 23 and 8, respectively. Using the validation samples that were not involved in modeling, the root mean square error (SEPV), relative root mean square error (R-SEPV) and correlation coefficients (RP, V) of prediction were 11.66 mg·kg-1, 10.7% and 0.722, respectively. The results indicated that the feasibility of using Vis-NIR spectroscopy combined with SG-PLS method to analyze soil Cr content. The constructed multi-parameters optimization platform with SG-PLS is expected to be applied to a wider field of analysis. The rapid detection method has important application values to large-scale agricultural production.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Identification of Soy Sauce Adulteration

The identification of soy sauce adulteration can avoid fraud, and protect the rights and interests of producers and consumers. Based on two measurement models (1 mm, 10 mm), the visible and near-infrared (Vis-NIR) spectroscopy combined with standard normal variate-partial least squares-discriminant analysis (SNV-PLS-DA) was used to establish the discriminant analysis models for adulterated and brewed soy sauces. Chubang soy sauce was selected as an identification brand (negative, 70). The adulteration samples (positive, 72) were prepared by mixing Chubang soy sauce and blended soy sauce with different adulteration rates. Among them, the “blended soy sauce” sample was concocted of salt water (NaCl), monosodium glutamate (C5H10NNaO5) and caramel color (C6H8O3). The rigorous calibration-prediction-validation sample design was adopted. For the case of 1 mm, five waveband models (visible, short-NIR, long-NIR, whole NIR and whole scanning regions) were established respectively;in the case of 10 mm, three waveband models (visible, short-NIR and visible-short-NIR regions) for unsaturated absorption were also established respectively. In independent validation, the models of all wavebands in the cases of 1 mm and 10 mm have achieved good discrimination effects. For the case of 1 mm, the visible model achieved the optimal validation effect, the validation recognition-accuracy rate (RARV) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RARV = 100%). The detection method does not require reagents and is fast and simple, which is easy to promote the application. The results can provide valuable reference for designing small dedicated spectrometers with different measurement modals and different spectral regions.

Biological Defenses against Ultra-Violet, Visible Light, and Near-Infrared Exposure

Objective: Humans are increasingly exposed to artificial light and electromagnetic wave radiation, in addition to solar radiation. Many studies have shown the biological effects of ultra-violet and near-infrared exposure, but few have extensively investigated the innate biological defenses within human tissues against visible light and near-infrared exposure. Herein, we investigated spectral properties of endogenous human biological defenses against ultra-violet to near-infrared. Methods: A double-beam spectrophotometer (190 - 2700 nm) was used to measure the transmission spectra of a saline solution used to imitate perspiration, and oil to imitate sebum, as well as human skin, blood, adipose tissue, and muscle. Results: Saline (thickness, 0.5 mm) blocked 27.5% - 98.6% of ultra-violet, 13.2% - 34.3% of visible light, and 10.7% - 99.8% of near-infrared. Oil (thickness, 0.5 mm) blocked 50.5% - 100% of ultra-violet, 28.7% - 54.8% of visible light, and 19.0% - 98.3% of near-infrared. Blood thicknesses of 0.05 and 0.5 mm blocked over 97.8%, 100% of ultra-violet, over 94.6%, 99.7% of visible light, and over 75.8%, 99.4% of near-infrared, respectively. Skin thicknesses of 0.25 and 0.5 mm blocked over 99.4%, 100% of ultra-violet and over 94.3%, 99.7% of visible light, and over 74.7%, 93.5% of near-infrared, respectively. Adipose tissue thickness of 0.25 and0.5 mm blocked over 98.3%, 100% of ultra-violet, over 94.7%, 99.7% of visible light, and over 88.1%, 98.6% of near-infrared, respectively. Muscle thickness of 0.25 and0.5 mm blocked over 95.4%, 99.8% of ultra-violet, over 93.1%, 99.5% of visible light, and over 86.9%, 98.3% of near-infrared, respectively. Conclusions: Humans possess endogenous biological protection against ultra-violet, visible light and near-infrared exposure on multiple levels, including through perspiration, sebum, blood, skin, adipose tissue, and muscle. Since solar and artificial radiation affects human tissues, biological defenses made of biological materials may be induced to protect subcutaneous tissues against these wavelengths.

An infrared and visible image fusion method based upon multi-scale and top-hat transforms

The high-frequency components in the traditional multi-scale transform method are approximately sparse, which can represent different information of the details. But in the low-frequency component, the coefficients around the zero value are very few, so we cannot sparsely represent low-frequency image information. The low-frequency component contains the main energy of the image and depicts the profile of the image. Direct fusion of the low-frequency component will not be conducive to obtain highly accurate fusion result. Therefore, this paper presents an infrared and visible image fusion method combining the multi-scale and top-hat transforms. On one hand, the new top-hat-transform can effectively extract the salient features of the low-frequency component. On the other hand, the multi-scale transform can extract highfrequency detailed information in multiple scales and from diverse directions. The combination of the two methods is conducive to the acquisition of more characteristics and more accurate fusion results. Among them, for the low-frequency component, a new type of top-hat transform is used to extract low-frequency features, and then different fusion rules are applied to fuse the low-frequency features and low-frequency background; for high-frequency components, the product of characteristics method is used to integrate the detailed information in high-frequency. Experimental results show that the proposed algorithm can obtain more detailed information and clearer infrared target fusion results than the traditional multiscale transform methods. Compared with the state-of-the-art fusion methods based on sparse representation, the proposed algorithm is simple and efficacious, and the time consumption is significantly reduced.

Intelligent Fusion of Infrared and Visible Image Data Based on Convolutional Sparse Representation and Improved Pulse-Coupled Neural Network

Multi-source information can be obtained through the fusion of infrared images and visible light images,which have the characteristics of complementary information.However,the existing acquisition methods of fusion images have disadvantages such as blurred edges,low contrast,and loss of details.Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform(NSST).Furthermore,the low-frequency subbands were fused by convolutional sparse representation(CSR),and the high-frequency subbands were fused by an improved pulse coupled neural network(IPCNN)algorithm,which can effectively solve the problem of difficulty in setting parameters of the traditional PCNN algorithm,improving the performance of sparse representation with details injection.The result reveals that the proposed method in this paper has more advantages than the existing mainstream fusion algorithms in terms of visual effects and objective indicators.

Sub-Regional Infrared-Visible Image Fusion Using Multi-Scale Transformation

Infrared-visible image fusion plays an important role in multi-source data fusion,which has the advantage of integrating useful information from multi-source sensors.However,there are still challenges in target enhancement and visual improvement.To deal with these problems,a sub-regional infrared-visible image fusion method(SRF)is proposed.First,morphology and threshold segmentation is applied to extract targets interested in infrared images.Second,the infrared back-ground is reconstructed based on extracted targets and the visible image.Finally,target and back-ground regions are fused using a multi-scale transform.Experimental results are obtained using public data for comparison and evaluation,which demonstrate that the proposed SRF has poten-tial benefits over other methods.

Infrared-to-visible and infrared-to-violet upconversion fluorescence of rare earth doped LaF_3 nanocrystals

This paper reports that hexagonal-phase LaF3:Yb30.20,Er30.02 and LaF3:Yb30.20,Tm30.02 nanocrystals (NCs) were synthesized via a hydrothermal method.The transmission electron microscopy,selected area electron diffraction,powder x-ray diffraction,and thermogravimetric analysis are used to characterize the NCs.Under 980 nm excitation,the Yb3/Er3 and Yb3/Tm3 codoped NCs colloidal solutions present bright green and blue upconversion fluorescence,respectively.These NCs show efficient infrared-to-violet and infrared-to-visible upconversion.The upconversion fluorescence mechanisms of LaF 3:Yb30.20,Er30.02 and LaF3:Yb30.20,Tm30.02 NCs are investigated with a 980-nm diode laser as excitation source.

Fusion of Infrared and Visible Images Using Fuzzy Based Siamese Convolutional Network

Traditional techniques based on image fusion are arduous in integrating complementary or heterogeneous infrared(IR)/visible(VS)images.Dissimilarities in various kind of features in these images are vital to preserve in the single fused image.Hence,simultaneous preservation of both the aspects at the same time is a challenging task.However,most of the existing methods utilize the manual extraction of features;and manual complicated designing of fusion rules resulted in a blurry artifact in the fused image.Therefore,this study has proposed a hybrid algorithm for the integration of multi-features among two heterogeneous images.Firstly,fuzzification of two IR/VS images has been done by feeding it to the fuzzy sets to remove the uncertainty present in the background and object of interest of the image.Secondly,images have been learned by two parallel branches of the siamese convolutional neural network(CNN)to extract prominent features from the images as well as high-frequency information to produce focus maps containing source image information.Finally,the obtained focused maps which contained the detailed integrated information are directly mapped with the source image via pixelwise strategy to result in fused image.Different parameters have been used to evaluate the performance of the proposed image fusion by achieving 1.008 for mutual information(MI),0.841 for entropy(EG),0.655 for edge information(EI),0.652 for human perception(HP),and 0.980 for image structural similarity(ISS).Experimental results have shown that the proposed technique has attained the best qualitative and quantitative results using 78 publically available images in comparison to the existing discrete cosine transform(DCT),anisotropic diffusion&karhunen-loeve(ADKL),guided filter(GF),random walk(RW),principal component analysis(PCA),and convolutional neural network(CNN)methods.

基于特征相似性的红外与可见光图像融合方法

单一图像无法全面描述目标的信息,实际应用价值低,针对当前红外与可见光图像融合方法存在的一些不足,如:融合质量差等,为了获得更加理想的红外与可见光图像融合效果,提出了基于特征相似性的红外与可见光图像融合方法。首先分析当前红外与可见光图像融合的研究进展,指出各种方法的局限性,然后采用红外图像和可见光图像,并对它们进行图像去噪、增强处理,采用卷积神经网络提取红外与可见光图像的特征,最后根据特征相似性进行红外与可见光图像融合,并对红外与可见光图像融合效果进行了测试,结果表明,本方法提升了红外与可见光图像融合质量,融合效果要明显优于其他红外与可见光图像融合方法。

深度学习与图像融合的行人检测算法研究

为解决智能辅助驾驶技术中可见光摄像机受光照和气候影响而导致行人目标识别困难的问题。通过研究图像融合技术,结合深度卷积神经网络,实现并改进了一种道路行人目标检测算法。方法是利用多源传感器图像融合技术,采用可见光相机与红外热成像相机融合的策略,以Faster RCNN算法为基础,从改进网络结构、特征融合、优化模型训练等方面展开研究,对复杂环境下的行人检测与定位跟踪展开研究,提出一种基于图像融合技术和改进的深度卷积神经网络的道路行人目标检测算法。实验结果表明,该算法对复杂气候环境下行人目标检测提高了检测效率和准确率,增加了智能辅助驾驶汽车的安全性。

基于跨模态近邻损失的可视-红外行人重识别

可视-红外跨模态行人重识别任务的目标是给定一个模态的特定人员图像,在其他不同模态摄像机所拍摄的图像集中进行检索,找出相同人员对应的图像。由于成像方式不同,不同模态的图像之间存在明显的模态差异。为此,从度量学习的角度出发,对损失函数进行改进以获取具有更加辨别性的信息。对图像特征内聚性进行理论分析,并在此基础上提出一种基于内聚性分析和跨模态近邻损失函数的重识别方法,以加强不同模态样本的内聚性。将跨模态困难样本的相似性度量问题转化为跨模态最近邻样本对和同模态样本对的相似性度量,使得网络对模态内聚性的优化更加高效和稳定。对所提方法在全局特征表示的基线网络和部分特征表示的基线网络上进行实验验证结果表明:所提方法对可视-红外行人重识别的预测结果相较于基线方法,平均准确度最高可提升8.44%,证明了方法在不同网络架构中的通用性;同时,以较小的模型复杂度和较低的计算量为代价,实现了可靠的跨模态行人重识别结果。

基于多成熟度光谱信息融合的阿森泰克苹果品质预测模型研究

不同成熟度的阿森泰克苹果品质变化大,会显著影响采后贮藏与销售效益。本研究以江苏宿迁四个成熟阶段的阿森泰克苹果为研究对象,首先利用主成分分析(principal component analysis,PCA)和线性判别分析(linear discriminant analysis,LDA)分析其色泽(L^(*)、a^(*)、b^(*)值)、硬度(firmness,FI)、可溶性固形物(soluble solid content,SSC)、可滴定酸(titratable acidity,TA)、水分含量(moisture content,MC)、干物质(dry matter content,DMC)的变化规律;同时,基于可见-近红外(visible and near-infrared,Vis-NIR)与近红外(nearinfrared,NIR)光谱技术结合连续投影(successiveprojectionsalgorithm,SPA)、竞争性自适应重加权(competitive adaptive reweighted sampling,CARS)、无信息变量消除(uninformative variable elimination,UVE)算法进行相关特征变量筛选,基于偏最小二乘(partial least squares,PLS)与支持向量机(support vector machine,SVM)建立阿森泰克苹果品质预测模型。结果表明,SSC、a^(*)、L^(*)、b^(*)对不同成熟度阿森泰克苹果的聚类贡献率较高,510~680、1170~1270、2300 nm为高相关度特征波段。SPA-PLS、SPA-SVM模型能很好地预测不同成熟度阿森泰克的L^(*)、b^(*)、a^(*)值,相对预测偏差(relative percent deviation,RPD)均高于3.00,CARS-PLS模型可以很好地预测SSC,RPD为3.19,但FI、TA、MC、DMC的SPA-PLS模型预测精度相对较低,RPD分别为2.27、2.21、2.32、2.42。研究结果证明Vis-NIR和NIR光谱方法能够预测不同成熟度阿森泰克苹果品质,为阿森泰克苹果采收管理与质量安全控制提供技术参考。