Analysis of Wide-Bandgap Material OPFET UV Detectors for High Dynamic Range Imaging and Communication Applications

The ultraviolet (UV) photoresponses of Wurtzite GaN, ZnO, and 6H-SiC-based Optical Field Effect Transistor (OPFET) detectors are estimated with an in-depth analysis of the same considering the generalized model and the front-illuminated model for high resolution imaging and UV communication applications. The gate materials considered for the proposed study are gold (Au) and Indium-Tin-Oxide (ITO) for GaN, Au for SiC, and Au and silver dioxide (AgO2) for ZnO. The results indicate significant improvement in the Linear Dynamic Range (LDR) over the previously investigated GaN OPFET (buried-gate, front-illuminated and generalized) models with Au gate. The generalized model has superior dynamic range than the front-illuminated model. In terms of responsivity, all the models including buried-gate OPFET exhibit high and comparable photoresponses. Buried-gate devices on the whole, exhibit faster response than the surface gate models except in the AgO2-ZnO generalized OPFET model wherein the switching time is the lowest. The generalized model enables faster switching than the front-illuminated model. The switching times in all the cases are of the order of nanoseconds to picoseconds. The SiC generalized OPFET model shows the highest 3-dB bandwidths of 11.88 GHz, 36.2 GHz, and 364 GHz, and modest unity-gain cut-off frequencies of 4.62 GHz, 8.71 GHz, and 5.71 GHz at the optical power densities of 0.575 μW/cm2, 0.575 mW/cm2, and 0.575 W/cm2 respectively. These are in overall, the highest detection-cum-amplifi-cation bandwidths among all the investigated devices. The same device exhibits the highest LDR of 73.3 dB. The device performance is superior to most of the other existing detectors along with comparable LDR, thus, emerging as a high performance photodetector for imaging and communication applications. All the detectors show considerably high detectivities owing to the high responsivity values. The results have been analyzed by the photovoltaic and the photoconductive effects, and the series resistance effects and will aid in conducting further research. The results are in line with the experiments and the commercially available software simulations. The devices will greatly contribute towards single photon counting, high resolution imaging, and UV communication applications.

Multi-exposure fusion for high dynamic range scene

Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high quality images in high dynamic range scene. First,a set of multi-exposure images is obtained by multiple exposures in a same scene and their brightness condition is analyzed. Then,multi-exposure images under the same scene are decomposed using dual-tree complex wavelet transform( DT-CWT),and their low and high frequency components are obtained. Weight maps according to the brightness condition are assigned to the low components for fusion. Maximizing the region Sum Modified-Laplacian( SML) is adopted for high-frequency components fusing. Finally,the fused image is acquired by subjecting the low and high frequency coefficients to inverse DT-CWT.Experimental results show that the proposed approach generates high quality results with uniform distributed brightness and rich details. The proposed method is efficient and robust in varies scenes.

Distortion-Free Data Embedding Scheme for High Dynamic Range Images

Distortion-free data embedding is a technique which can assure that not only the secret data is correctly extracted but also the cover media is recovered without any distortion after secret data is extracted completely. Because of these advantages, this technique attracts the attention of many researchers. In this paper, a new distortion-free data embedding scheme for high dynamic range （HDR） images is proposed. By depending on Cartesian product, this scheme can obtain higher embedding capacity while maintaining the exactly identical cover image and stego image when using the tone mapping algorithms. In experimental results, the proposed scheme is superior to Yu et aL＇s scheme in regard to the embedding rate——an average embedding rate of 0.1355 bpp compared with Yn et aL＇s scheme （0.1270 bpp）.

Shape recovery using high dynamic range images

An effective method for object shape recovery using HDRIs （high dynamic range images） is proposed. The radiance values of each point on the reference sphere and target object are firstly calculated, thus the set of candidate normals of each target point are found by comparing its radiance to that of each reference sphere point. In single-image shape recovery, a smoothness operation is applied to the target normals to obtain a stable and reasonable result; while in photometric stereo, radiance vectors of reference and target objects formed due to illuminations under different fight source directions are directly compared to get the most suitable target normals. Finally, the height values can be recovered from the resulting normal field. Because diffuse and specular reflection are handled in an unified framework with radiance, our approach eliminates the limitation presented in most recovery strategies, i.e., only Lambertian model can be used. The experiment results from the real and synthesized images show the performance of our approach.

Fast Scheme for Projective Geometric Correction and Edge Blending Based on High Dynamic Range Images

With the development of graphic processing unit(GPU)power,it is now possible to implement geometric correction and edge blending functions on a single computer.However,the processing resources consumed by the geometric correction and edge blending phases still burden the system and slow down the main application considerably.A new platform independent scheme is proposed,minimizing the negative influence on performance.In this scheme,parameters for geometric correction and edge blending are firstly defined in an interactive way and recorded as a 32-bit high dynamic range(HDR) image,which is then used by high level shading language(HLSL) codes embedded in the main application as a lookup table,greatly reducing the computational complexity and enhancing flexibility.

High Dynamic Range Image Fusion Based on Wavelet

With the developpment of image fusion technology and the maturity of wavelet theory, wavelet transform with its good time-frequency characteristics stands out in the field of image fusion. On the basis of wavelet transforms theory, this article proposes a high dynamic range imaging confusion method which combines with wavelet decomposition. First, perform a wavelet multi-scale decomposition to the two registered source image; then conduct wavelet inverse transform to the decomposed images. This paper focuses on the characteristics of high frequency and low frequency domain after wavelet decomposition,using different fusion methods in each of the frequency domain, finally obtain the fused image through inverse wavelet transform image reconstruction. The simulation results and evaluation index results show that, compared with other similar methods, this method is better in retaining the original image's details information, and improves the quality of fusion image.

Novel Logarithmic Active Pixel Sensor with High Dynamic Range and High Output Swing

The logarithmic response complementary metal oxide semiconductor （CMOS） image sensor provides a wide dynamic range, but its drawback is the lack of simple fixed pattern noise（FPN） cancellation scheme. Designed is a novel logarithmic active pixel sensor（APS） with high dynamic range and high output swing. Firstly, the operation principle of mixed-model APS is introduced. The pixel can work in three operation modes by choosing the proper control signals. Then, FPN sources of logarithmic APS are analyzed, and double-sampled technique is implemented to reduce FPN. Finally, according to the simulation results, layout is designed and has passed design rule check（DRC）, electronic rule eheck（ERC） and layout versus schematic（LVS） verifications, and the post-simulation results are basically in agreement with the simulation results. Dynamic range of the new logarithmic APS can reach about 140 dB; and the output swing is about 750 inV. Results show that by using double sampled technique, most FPN is eliminated and the dynamic range is enhanced.

极端曝光图像的多尺度分解细节感知融合算法

针对极端曝光(欠曝光和过曝光)图像动态范围低的问题,提出一种基于多尺度分解细节感知的图像融合算法。欠曝光图像经过细节增强后,与过曝光图像进行初步的粗融合;采用小波变换对细节增强后图像的亮度分量进行多尺度分解,并设计专门的高频和低频融合策略,实现亮度分量的精融合;将粗融合图像的色调、饱和度分量,与精融合图像的亮度分量重组,获得最终的融合结果。基于大量测试数据,实验结果表明所提方法在视觉效果方面表现优异,平均MEF-SSIM指标为0.9854,平均SSIM指标为0.6508,均优于现有主流算法。

星载高动态范围差分吸收成像光谱仪成像电路设计及实现

星载差分吸收成像光谱仪通常运行于太阳同步轨道,探测地表反射光的光谱并根据特征气体的谱线吸收强度测量区域内某些痕量气体浓度。第一代星载差分吸收光谱仪受限于探测器性能等原因,探测的动态范围不大,造成暗背景下数据信噪比低,亮背景下像元饱和。随着更大阱深,更高速度的探测器出现,第二代高动态范围的差分吸收成像光谱仪得以实现。本文介绍了高动态范围的差分吸收光谱仪成像电路的研制过程,包括器件选型,电路方案及电路测试,并对测试结果进行了分析。重点讨论了高动态范围的实现,给出了单点测量值的概念,提出了最大单点测量值的计算公式,解释了最大单点测量值与动态范围的关系,并评估了探测器件的单点探测能力,同时规划了未来进一步提高动态范围的方法,从而为同类设备的研制提供参考。

人工智能在图像增强技术中的应用

电视技术在人工智能时代(AI,Artificial Intelligence)迎来了新的发展机遇。其中图像增强技术在AI的赋能下,取得了前所未有的进展。本文面向超分辨率、低照度增强和高动态范围三种常见的图像增强技术,以本团队的相关工作为线索,从一个切面介绍“AI+广电”发展现状,并就其发展趋势展开思考。

基于一致性感知特征融合的高动态范围成像方法

高动态范围成像(High Dynamic Range Imaging,HDRI)技术是指通过融合多张低动态范围(Low Dynamic Range,LDR)图像拓展图像动态范围、完整图像内容的方法,其为解决由于相机传感器动态范围有限而导致所拍摄图像内容丢失的问题提供了实际的解决方案.通过数十年的研究,众多有效的HDRI方法已被提出,并在无物体运动、内容曝光良好的静态场景中取得接近最优的性能.然而,现实场景中物体移动和相机偏移无法避免,直接使用传统HDRI方法会在融合后的HDR图像中产生严重的重影和伪影.这使得仅包含简单融合过程的HDRI方法并不适用于实际应用,现实场景中的HDRI任务仍然具有一定挑战.因此,针对动态场景下的HDRI研究迅速发展.近期的方法集中在借助深度卷积神经网络(Convolutional Neural Network,CNN)的力量以期实现更好的性能.在这些基于CNN的方法中,特征融合对于恢复图像完整内容、消除图像伪影方面起着至关重要的作用.传统的特征融合方法通过借助跳跃连接或注意力模块,首先将LDR图像的特征进行拼接,并通过堆叠的卷积操作逐渐关注不同的局部特征.然而,此类方案通常忽略了LDR图像序列之间丰富的上下文依赖关系,且未充分利用特征之间的纹理一致性.为解决这一问题,本文提出了一种全新的一致性感知特征融合(Coherence-Aware Feature Aggregation,CAFA)方案,该方案在卷积过程中对输入特征中位于不同空间位置但具有相同上下文信息的特征信息进行采样,从而显式地将上下文一致性纳入特征融合中.基于CAFA,本文进一步提出了一种结合CAFA的动态场景下一致性感知高动态范围成像网络CAHDRNet.为更好地嵌合CAFA方案,本文通过设计三个额外的可学习模块来构建CAHDRNet.首先,使用基于在ImageNet上预训练的VGG-19构建可学习特征提取器,并在模型训练期间不断更新该特征提取器的参数.这种设计可实现LDR图像的联合特征学习,为CAFA中的上下文一致性评估奠定了坚实基础.接着,应用所提出的CAFA模块,通过在图像特征中采样具有相同上下文的信息进行特征融合.最后,本文提出使用一种多尺度残差补全模块来处理融合后的特征,利用不同扩张率进行特征学习,以实现更强大的特征表示并在图像缺失区域中进行可信细节填充.同时,设计一个软注意力模块来学习不同图像区域的重要性,以便在跳跃连接期间获得与参考图像互补的所需特征.多种实验验证了CAHDRNet的有效性并证实其优于现有最先进的方法.具体而言,本文所提出的CAHDRNet在Kalantari数据集上HDR-VDP-2和PSNR-L等指标相较于次好方法AHDRNet分别提升了1.61和0.68.

面向高动态范围成像的内容恢复和鬼影抑制网络

基于多曝光融合的高动态范围(High Dynamic Range,HDR)成像旨在整合多幅低动态范围图像(Low Dynamic Range,LDR)的信息,以生成高质量的HDR图像.然而,抑制运动区域的鬼影信息和恢复过饱和区域的缺失信息仍是HDR成像面临的两大挑战.为了综合考虑对参考图像缺失内容的恢复和运动区域鬼影的抑制,文中提出面向HDR成像的内容恢复和鬼影抑制网络.在内容恢复方面,引入基于预测滤波的内容恢复块.由内容恢复块预测到的滤波核对参考图像特征进行滤波,整合参考图像和非参考图像中的关键信息,为有效进行缺失内容的重建提供更丰富的信息.为了抑制运动区域的鬼影信息并充分利用非参考图像中的互补信息,引入可变形卷积,将非参考图像特征与参考图像特征对齐.此外,为了提升网络的HDR图像重建能力,构建三支路图像重建模块,包括一条主支路和两条辅助支路,辅助支路协助主支路在HDR结果的生成过程中更好地保留细节.实验表明,文中网络在主观视觉和客观指标上均具有较优表现.

自适应平台直方图高动态范围红外图像显示方法

针对不同场景高动态范围红外图像的自适应显示方法问题,提出了一种基于图像分割的自适应平台直方图显示方法,首先进行图像分割,再根据分割结果讨论平台阈值的选取,并分析了平台阈值的选取依据。试验结果表明本文方法对不同场景均获得了良好显示效果,具备实用性。

动态场景下深度自监督多曝光图像融合方法

近年来,面向动态场景的多曝光图像融合技术取得重大进展.其中,基于深度学习的方法在视觉效果和运算效率上都远超传统算法,成为高动态范围成像技术的主流.然而,现有基于深度学习的融合方法都以有监督学习的方式实现,过度依赖真值图像,难以被广泛应用于实际场景中.本文提出了一个基于深度自监督学习的动态多曝光图像融合网络,主要贡献包括:设计自监督的动态多曝光融合网络框架,探索高动态范围图像与低动态范围图像序列的内在关联;提出基于注意力机制的全局去伪影模块,使用全局文本模块减少动态融合产生的运动伪影,增强图像细节;提出融合重建模块,通过残差和稠密连接实现多层次特征之间的信息流动;设计运动掩膜引导的自监督损失函数,用于网络的高效训练.实验表明,与现有方法相比,本文提出的方法在高动态范围图像重建的主观和客观质量上均表现较好,运算效率显著提升.

基于事件信息与深度学习的高动态范围三维重建

采用光学三维成像技术测量金属零件、黑色物体以及半透明物体等高动态范围(High dynamic range,HDR)表面的三维轮廓是一个极具挑战性的问题。目前,传统方法对存在较低反射以及半透明区域的场景进行重建还有一定的局限性,半透明物体的内部反射噪声很难消除。现有基于深度学习的方法通常使用相对较强的激光强度,这可能会损坏样品,同时会出现采集图像过曝现象,需要对激光强度进行繁琐的调整。针对这些问题,本文提出基于事件信息和深度学习算法的高动态场景三维测量方法。事件相机通过异步记录单个像素的亮度变化,无需等待全局曝光时间,具有高动态响应范围,能够充分采集到HDR场景的激光条纹反射信息。引入深度卷积神经网络(Deep convolutional neural network,DCNN)来消除半透明物体的内部噪声以及金属物体高反光的过曝影响,同时增强弱激光条纹图像质量。实验结果表明,本文方法能够应用低功率线激光扫描成功实现HDR场景的高质量三维重建。

基于深度学习的显著运动下高动态范围图像重建的研究

高清直播业务在广电行业中举足轻重,但是存在不同强度光照以及人物运动导致直播画面不清晰和亮度不自然的问题,本文提出了一种基于深度学习技术的方法,可应用于显著运动下的高动态范围图像重建。实验结果表明,该方法不仅改善了画面清晰度,还解决了亮度不均和运动模糊等问题,进一步提升了用户的直播观看体验。此外,该方法还大幅降低了模型计算成本,为智能高清直播技术的进一步推进提供了有力支持。

基于密集连接编解码网络的HDR重建算法

针对动态场景下高动态范围(High Dynamic Range,HDR)重建会出现伪影现象的问题,提出一种基于密集连接编解码网络的HDR重建算法。该算法基于通道和空间维度的注意力机制模块,根据注意力特征图引导并提取不同曝光低动态范围图像相对应的图像特征,自动抑制由失准和过饱和引起的不期望分量,增强非参考图像中所期望的精细细节。通过密集连接编解码网络模块,在编码部分逐步提取特征图的多层次细节,并利用密集连接增强信息交互,使用解码器还原图像细节特征,最终生成少伪影、高质量的高动态范围图像。实验结果表明,该算法可以有效减弱伪影现象,得到细节丰富的高动态范围图像。

8K电视信号的HDR和色域扩展技术

探讨8K电视技术中高动态范围(High Dynamic Range,HDR)和色域扩展技术的应用。首先,介绍8K电视的定义、发展历程、应用领域和基本特性。其次,着重探讨HDR技术的概念和在8K电视中的实现方式,以及色域扩展技术的定义、原理、标准和在8K电视中的应用方法。通过实例验证HDR和色域扩展技术对图像质量的提升效果。最后,总结研究成果,强调8K电视技术在提供卓越视觉体验方面的潜力和重要性。

一种新型高动态范围CMOS图像传感器结构设计

阐述一种新型的高动态范围CMOS图像传感器结构。在新型CMOS图像传感器中,像素包括两个具有不同感光灵敏度的光电二极管以及多个电荷-电压转换增益档位,列电路具有多个模拟电压增益档位。根据该结构和读出方法,对每个像素只进行一次信号电压的读取,可以显著缩短行读出时间,进而提高帧率。

基于局部细节增强的快速多曝光图像融合算法

当前基于细节增强机制的多曝光图像融合方法效率较低,针对此问题,提出一种基于局部细节增强的快速多曝光图像融合算法。首先,改进每幅图像待增强区域的划定方法;然后,采用快速局部拉普拉斯滤波(FLLF)提取待增强区域的细节信息,并根据采样插值原理对增强过程进行优化加速,减少计算局部拉普拉斯金字塔系数时的采样次数,避免细节过度增强的同时,进一步提高融合效率。对40组多曝光序列的融合质量和平均运行时间进行测试,并与5种代表性算法进行对比实验。结果表明,算法不仅保持超亮超暗区域的细节信息,而且视觉效果更加自然。算法的两种客观质量指标MEF-SSIM值和Q CB分别为0.942和0.441,在6种算法中分别排名第2和第1;运行时间比基于原始分辨率空间运算的最快方法Li2020减少了46.2%,具有较高的运行效率。