晕苯增强CMOS图像传感器

由于紫外光在硅中的穿透深度有限，以及多晶硅栅极对紫外光的吸收，导致传统的硅基CMOS图像传感器在紫外光波段的响应不高。在此，本文选择一种低成本的下转换法来提升CMOS图像传感器的紫外响应能力，采用真空热蒸发法分别在石英衬底和CMOS图像传感器的像敏面上蒸镀了晕苯薄膜，并对薄膜的光学性能、红外光谱、光稳定性和热稳定性进行了研究。实验结果表明，晕苯薄膜能吸收紫外光并发射出500 nm的绿色荧光，可以与CMOS图像传感器的光谱响应峰值很好地匹配；同时，发现晕苯红外吸收光谱的实验值和计算值基本吻合；薄膜在200 ℃温度下退火20 min后，其发射峰的荧光强度保持在原来的95.7%；在280 nm激发波长照射大约60 min后，发光强度呈指数衰减至初始值的64%。采用CMOS单色相机在可见光（400~780 nm）和紫外光（365 nm）下定性分析了薄膜的紫外增强效果，发现蒸镀晕苯薄膜后的CMOS单色相机可以提高对紫外光的灵敏度。     

夜视抗晕光融合图像自适应分区质量评价

针对夜视晕光场景中,高亮度晕光信息导致现有红外与可见光融合图像评价方法失效的问题,该文提出一种自适应分区的融合图像质量评价方法。该方法根据可见光图像的晕光程度自动确定自适应系数,并通过迭代计算可见光灰度图像的晕光临界灰度值,将融合图像自动分为多个晕光区和非晕光区;在晕光区由设计的晕光消除度指标评价融合图像的晕光消除效果;在非晕光区从融合图像自身特性、对原始图像信息保留程度以及人眼视觉效果3方面评价融合图像纹理色彩等细节信息的增强效果;通过对4种不同抗晕光算法的融合图像进行评价分析,甄选出9种客观评价指标构成夜视抗晕光融合图像质量评价体系。不同夜视晕光场景下的实验结果表明,所提方法能够全面、合理地评价红外与可见光融合的抗晕光图像质量,解决了融合图像晕光消除越彻底客观评价结果反而越差的问题,也适于评判不同抗晕光融合算法的优劣。     

TDI图像传感器横向抗晕栅极电压与满阱容量关系研究

时间延时积分CMOS图像传感器(TDI-CIS)具有优良的微光探测能力,可应用于航空探测及卫星遥感等领域。然而,在入射光强较强时,TDI-CIS容易出现光晕(Blooming)现象,影响观测效果。首先分析了光晕产生的机理;然后基于两种传统的抗晕结构,设计出一种具有沿垂直方向布局的长方形横向抗晕栅的TDI-CIS;通过成像实验发现横向抗晕栅极电压与抗晕效果及满阱容量(FWC)之间呈负相关关系;最后通过实验得到所设计TDI-CIS的最优抗晕栅极电压值为2.1V。     

三值光计算机解码模拟器的图像采集系统

针对三值光计算机解码模拟器需要对光学运算器的运算结果进行高速、可靠、实时地采样、A/D转换和图像采集问题,设计研制了一个USB型图像采集系统.该系统以嵌入式微处理器S3C44B0X和CMOS图像传感器OV7660为核心.前端图像光信号采样、A/D转换和数字数据输出处理采用CMOS图像传感器OV7660完成, 后端的图像采集和传输采用嵌入式微处理器S3C44B0X和USB2.0,使系统具有高速、实时和连接灵活方便的特点.为保证图像采集和传输的可靠性,采用了事务处理机制.实验结果表明,系统工作稳定、性能良好,处理速度可达20MB/s.     

CMOS图像传感器的图像采集系统的研究与实现

研究一种基于USB2D的CMOS图像传感器的图像采集系统的实现方案.以xilinx公司的FPGA芯片为核心控制芯片,Cypress公司的CY7C68013为USBZ0接口芯片.主要介绍了CMOS图像传感器外围电路设计、FPGA芯片与CMOS图像传感器接口电路设计、FPGA芯片与USB模块的接口电路设计.所设计的采集系统功耗低、成本低、可扩展性强.     

基于多核异构的异源图像融合抗晕光系统设计

针对异源图像融合抗晕光算法复杂,应用于视频实时处理困难,设计了基于嵌入式多核异构架构的异源图像融合抗晕光系统。将系统控制和图像融合抗晕光算法分别在ARM和FPGA中实现,解决了数据串行和任务顺序执行导致的系统实时性问题,消除了融合图像中的晕光痕迹。经实验验证,多核异构的抗晕光系统晕光消除度和系统实时性均满足系统需求。     

可见光与红外融合的汽车抗晕光图像评价方法

为了解决汽车抗晕光场景中,融合图像质量的客观评价与人眼视觉效果不一致的问题,提出了一种新的可见光与红外融合图像质量评价方法,该方法通过设计自适应迭代阈值法自动确定融合图像的晕光临界灰度值,并将融合图像自动分为晕光区和非晕光区。针对晕光区,设计晕光消除度指标评价晕光消除的效果;针对非晕光区,从多方面评价色彩、细节信息的增强效果,并甄选出合适的指标构成完整的图像质量评价体系。为验证该方法的合理性,对采用4种不同算法的融合图像进行评价,实验分析表明,该方法的主客观评价结果一致,适于评判不同可见光与红外融合的抗晕光图像质量及算法的优劣。     

基于FPGA的数字图像采集存储系统的设计

本系统主要利用FPGA实现了对CMOS图像传感器的配置及图像采集,将采集到的数字图像实时存储到Flash存储器(K9NBG08U5A)中.同时对CMOS图像传感器捕获的图像进行实时传输、存储、显示.通过采集存储实验,验证了该系统的可行性和实时性.     

黑白CMOS图像传感器OV9120的原理及应用

本文阐述了CMOS图像传感器的一般特征,详细介绍了黑白CMOS图像传感器芯片OV9120的性能、特点及工作原理,给出了OV9120在图像采集处理中的具体应用实例.     

一种新型CMOS图像传感器的设计

为了提高CMOS图像传感器的图像质量,通过对图像主要的噪声源以及图像失真的分析,提出一种新型的CMOS有源像素图像传感器.该CMOS图像传感器使用4T有源像素,大大提高了图像传感器的灵敏度.通过在传感器中集成图像预处理功能,对改善图像的质量起到了很好的效果.     

Spatial-domain image hiding using image differencing

A method to embed a secret image into a cover image is proposed. The method is based on the similarity among the grey values of consecutive image pixels as well as the human visual system's variation insensitivity from smooth to contrastive. A stego-image is produced by replacing the grey values of a differencing result obtained from the cover image with those of a differencing result obtained from the secret image. The process preserves the secret image with no loss and produces the stego-image with low degradation. Moreover, a pseudorandom mechanism is used to achieve cryptography. It is found from experiment that the peak values of signal-to-noise ratios of the method are high and that the resulting stego-images are imperceptible. Even when the size of the secret image is about a half of the cover image     

基于多层级图像分解的图像融合算法

不同类型的探测器在成像机理上有不同的侧重点,使得成像图像表征的信息也有所不同,导致单幅图像不能完整地反映场景的有效信息。因此,提取多源图像的互补信息,并去除其中的冗余信息,合成一幅能准确、完整表达场景的复合图像的技术成为了图像处理领域中一项非常重要的技术,图像融合正是这类问题的一种有效解决方法。针对传统多尺度分解的图像融合方法易产生噪声和信息缺失的现象,文中提出了一种基于多层级图像分解的红外与可见光图像融合算法。首先,利用加权平均曲率滤波的边缘保持特性与高斯滤波的平滑特性,构建了多层级图像分解模型。在利用该模型将源图像分解为小尺度层、大尺度层和基层等3个不同层级。然后,针对基层,采用能量属性融合策略进行融合;针对大尺度层,采用复合融合策略进行融合;针对小尺度层,采用最大值融合策略。最后,将融合后的层级进行加和,以重构出最终的融合图像。实验结果表明:文中提出的基于多层级图像分解的图像融合算法能够有效降低噪声产生的概率,同时减少了融合后的信息缺失。     

基于图像增强和复原的图像去雾方法研究

雾霭等天气下获得的图像存在对比度低、颜色退化、景物模糊等一系列图像退化的问题,直接影响了对图像信息的有效利用。因此,对雾天图像进行有效的去雾处理,有效改善降质图像的质量,具有一定的实际意义。分析讨论基于图像增强的多尺度Retinex算法和利用图像复原原理的基于暗原色先验理论的去雾算法,并对具有不同特点的单幅有雾图像进行去雾仿真。实验结果表明,不同理论基础的两种去雾算法各有特点,基于暗原色理论处理得到的图像去雾效果更显著,算法运行速度更快。     

Blind image deconvolution for single motion-blurred image

Motion blur due to camera shake during exposure is one of the most common reasons of image degradation,which usually reduces the quality of photographs seriously.Based on the statistical properties of the natural image's gradient and the blur kernel,a blind deconvolution algorithm is proposed to restore the motion-blurred image caused by camera shake,adopting the variational Bayesian estimation theory.In addition,the ring effect is one problem that is not avoided in the process of image deconvolution,and usually makes the visual effect of the restored image badly.So a dering method is put forward based on the sub-region detection and fuzzy filter.Tested on the real blurred photographs,the experimental results show that the proposed algorithm of blind image deconvolution can remove the camera-shake motion blur from the degraded image effectively,and can eliminate the ring effect better,while preserve the edges and details of the image well.     

Accurate image registration for MAP image super-resolution

The accuracy of image registration plays a dominant role in image super-resolution methods and in the related literature, landmark-based registration methods have gained increasing acceptance in this framework. In this work, we take advantage of a maximum a posteriori (MAP) scheme for image super-resolution in conjunction with the maximization of mutual information to improve image registration for super-resolution imaging. Local as well as global motion in the low-resolution images is considered. The overall scheme consists of two steps. At first, the low-resolution images are registered by establishing correspondences between image features. The second step is to fine-tune the registration parameters along with the high-resolution image estimation, using the maximization of mutual information criterion. Quantitative and qualitative results are reported indicating the effectiveness of the proposed scheme, which is evaluated with different image features and MAP image super-resolution computation methods.     

Fast image reconstruction network in image stitching

Compared with the traditional feature-based image stitching algorithm, the free-view image stitching algorithm based on deep learning has the advantages of fast stitching speed and good effect. However, these algorithms still cannot achieve real-time splicing speed. For the image reconstruction stage, we redesign a new fast image reconstruction network. This network is designed based on ShuffleNet, and the new network structure and loss function will reduce the time required for image reconstruction. In addition, this network can also reduce the performance loss after the network is lightweight. It is proved by experiments that the fast image reconstruction network can realize real-time high-resolution free-view image reconstruction.     

Prototype image constraints for set-theoretic image restoration

Constraints based on prototype images are developed and used in set-theoretic image restoration. A prototype can be obtained as a result of applying a predetermined operator to the observed image. In this case, the operator and the bound, which limits the variation of the restored image from the prototype, are the two defining quantities of a prototype constraint. General guidelines for rigorously estimating the defining bound of a prototype constraint under certain simplifying conditions are discussed. The authors provide two examples of prototype constraints where the prototypes are obtained by the Wiener filtering operator and a local averaging operator. The projection onto convex sets algorithm using the prototype constraints is applied to both monochrome and color images degraded by out-of-focus blur at different noise levels. The results show significant improvement over the Wiener restoration in reducing the restoration artifacts     

Underwater image enhancement with image colorfulness measure

Due to the absorption and scattering effects of the water, underwater images tend to suffer from many severe problems, such as low contrast, grayed out colors and blurring content. To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model. Two parts constitute the overall model. The first one is a non-parameter layer for the preliminary color correction, then the second part is consisted of parametric layers for a self-adaptive refinement, namely the channel-wise linear shift. For better details, contrast and colorfulness, this enhancement network is jointly optimized by the pixel-level and characteristic-level training criteria. Through extensive experiments on natural underwater scenes, we show that the proposed method can get high quality enhancement results.     

Dual-branch deep image prior for image denoising

In this work, we propose a two-stage denoising approach, which includes generation and fusion stages. Specifically, in the generation stage, we first split the expanding path of the UNet backbone of the standard DIP (deep image prior) network into two branches, converting it into a Y-shaped network (YNet). Then we adopt the initial denoised images obtained with DAGL (dynamic attentive graph learning) and Restormer methods together with the given noisy image as the target images. Finally, we utilize the standard DIP on-line training routine to generate two complementary basic images, whose image quality is quite improved, with the help of a novel automatic iteration termination mechanism. In the fusion stage, we first split the contracting path of the standard UNet network into two branches for receiving the two basic images generated in the previous stage, and obtain a fused image as the final denoised image in a fully unsupervised manner. Extensive experimental results confirm that our method has a significant improvement over the standard DIP or other unsupervised methods, and outperforms recently proposed supervised denoising models. The noticeable performance improvement is attributed to the proposed hybrid strategy, i.e., we first adopt the supervised denoising methods to process the common content of images substantially, then utilize the unsupervised method to fine-tune the specific details. In other words, we take full advantage of the high performance of the supervised methods and the flexibility of the unsupervised methods.