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1.
Muhammad Attique Khan Tallha Akram Muhammad Sharif Tanzila Saba Kashif Javed Ikram Ullah Lali Urcun John Tanik Amjad Rehman 《Microscopy research and technique》2019,82(6):741-763
Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK‐1, ISIC MSK‐2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features. 相似文献
2.
针对在低光照环境下拍摄的图像受光照强度的影响而导致图像质量差的问题,本文提出了基于双边滤波的MSR与AutoMSRCR算法融合的低光照图像增强算法。首先,在原始低光照图像的HSV颜色空间中针对V分量使用基于双边滤波MSR算法对亮度通道进行增强,得到保留原有色彩信息的亮度增强图像。然后,将此初始亮度增强图像运用CLAHE算法基于LAB颜色空间进行亮度通道细节增强,得到细节增强的图像。最后,采用AutoMSRCR算法对原始低光照图像进行处理,并与细节增强图像进行加权融合得到最终的增强图像。以UCIQE,AG,SD,IE为评价指标,将经过该算法增强的图像与MSR算法,MSRCR算法,CLAHE算法,改进GAMMA算法等进行比较。结果表明,使用该图像增强算法处理的图像效果最佳,UCIQE达到了0.472 1,AG达到了12.674 2,SD达到了0.263 2,IE达到了7.637 9。增强后的图像色彩信息更加丰富,图像更加清晰,图像对比度更好,图像的边缘纹理信息保留更完整,图像质量更高,本研究为低光照图像增强提供了一种可行方法。 相似文献
3.
卷积神经网络在图像处理中发展迅速。大多数图像去雾算法仅专注于去雾,忽略了去雾图像的整体质量,进而导致诸如信息丢失和纹理模糊等问题。为此,提出了一种去雾和增强卷积神经网络。通过编码和解码获得雾层图像和一阶段去雾图像,增强网络用于恢复去雾图像的纹理和细节。实验表明,该方法在主观评价和质量指标上均具有优异的效果,获得了去雾程度更加彻底、细节和纹理更加清晰的去雾图像,有效地解决了信息丢失和纹理模糊的问题。 相似文献
4.
针对光线在水体中传播会受到吸收和散射影响,仅使用传统的强度相机获取水下图像存在成像结果亮度偏低、图像模糊、细节丢失等问题,将深度融合网络应用于水下偏振图像,即用深度学习的方法将水下偏振度图像与光强图像融合。分析水下主动偏振成像模型,搭建实验装置获取水下偏振图像构建训练数据集,并进行适当变换以扩充数据集。构建基于无监督学习和注意力机制引导的用于融合偏振度和光强图像的端到端学习框架,并对损失函数及权重参数进行阐述。使用制作的数据集在不同的损失权重参数下进行网络训练,基于不同的图像评价指标对融合后的图像进行评估。实验结果表明,融合后的水下图像细节更为丰富,相比于光强图像信息熵提升24.48%,标准差提升139%。相比于其他传统融合算法,该方法不需要人工调节权重参数,运算速度较快,具有较强的稳定性和自适应性,对于海洋探测、水下目标识别等领域的应用研究具有重要意义。 相似文献
5.
Farhat Afza Muhammad A. Khan Muhammad Sharif Amjad Rehman 《Microscopy research and technique》2019,82(9):1471-1488
Among precision medical techniques, medical image processing is rapidly growing as a successful tool for cancer detection. Skin cancer is one of the crucial cancer types. It is identified through computer vision (CV) techniques using dermoscopic images. The early diagnosis skin cancer from dermoscopic images can be decrease the mortality rate. We propose an automated system for skin lesion detection and classification based on statistical normal distribution and optimal feature selection. Local contrast is controlled using a brighter channel enhancement technique, and segmentation is performed through a statistical normal distribution approach. The multiplication law of probability is implemented for the fusion of segmented images. In the feature extraction phase, optimized histogram, optimized color, and gray level co‐occurrences matrices features are extracted and covariance‐based fusion is performed. Subsequently, optimal features are selected through a binary grasshopper optimization algorithm. The selected optimal features are finally fed to a classifier and evaluated on the ISBI 2016 and ISBI 2017 data sets. Classification accuracy is computed using different Support Vector Machine (SVM) kernel functions, and the best accuracy is obtained for the cubic function. The average accuracies of the proposed segmentation on the PH2 and ISBI 2016 data sets are 93.79 and 96.04%, respectively, for an image size 512 × 512. The accuracies of the proposed classification on the ISBI 2016 and ISBI 2017 data sets are 93.80 and 93.70%, respectively. The proposed system outperforms existing methods on selected data sets. 相似文献
6.
针对低照度全景图像存在的对比度低、视觉效果差等问题,提出了一种基于模拟多曝光融合的低照度全景图像增强算法。首先,将原图像从RGB颜色空间转换到HSV颜色空间,以图像信息熵作为度量估计最佳曝光率,采用亮度映射函数对V分量进行增强处理,再将其转回RGB颜色空间得到过曝光图像;接着,以低照度图像和过曝光图像为输入,采用曝光插值法合成中等曝光图像;然后,采用多尺度融合策略将低照度图像、中等曝光图像和过曝光图像进行融合,得到融合后的图像;最后,通过多尺度细节增强算法对融合后的图像进行细节增强,得到最终的增强图像。通过与NPE,LIME,SRIE,Li,Ying,RtinexNet算法相比,在不同场景的全景图像上,亮度顺序误差(LOE)最小为322,自然图像质量评估器(NIQE)最小为2.32,无参考空间域图像质量评估器最小为5.71,结构相似度(SSIM)最高达到0.82,综合性能优于其他对比算法。实验结果表明,本文算法能够有效地提升低照度全景图像的质量。 相似文献
7.
针对单光子计数成像技术探测目标信号微弱信噪比低、所得图像目标区域不清楚、背景噪声严重等问题。 本文利用270±5 nm 的日盲紫外滤光片、图像增益 7105 的微通道板像增强器(MCP)、荧光屏和最大分辨率为 1 504×1 504 的科学级互补金属氧化物半导体(sCMOS)等器件,设计了日盲紫外单光子探测系统,并通过时序控制获取了单光子光斑图像。 为了突出图像中的目标区域,本文利用改进的形态学高帽变换算法,对光斑目标区域进行增强处理;随后利用三角阈值法对图像进行二值化处理,同时利用连通域对目标区域的坐标进行提取;最后运用区域极值算法在原图中的目标区域进行单光子计数。 对紫外光源进行了单次曝光时间为 80~100 ns 的系列成像和数据处理实验,实验结果验证了所设计的单光子成像探测系统和光子计数算法的可行性。 相似文献
8.
多聚焦图像融合技术是为了突破传统相机景深的限制,将焦点不同的多幅图像合成一幅全聚焦图像,以获得更加全面的信息。以往基于空间域和基于变换域的方法,需要手动进行活动水平的测量和融合规则的设计,较为复杂。所提出的方法与传统的神经网络相比增加了提取浅层特征信息的部分,提高了分类准确率。将源图像输入训练好的多尺度特征网络中获得初始焦点图,然后对焦点图进行后处理,最后使用逐像素加权平均规则获得全聚焦融合图像。实验结果表明,本文方法融合而成的全聚焦图像清晰度高,保有细节丰富且失真度小,主、客观评价结果均优于其他方法。 相似文献
9.
Bin HAN Hao WANG Xin LUO Chengyuan LIANG Xin YANG Shuang LIU Yicheng LIN 《Frontiers of Mechanical Engineering》2022,17(3):13
Clear, correct imaging is a prerequisite for underwater operations. In real freshwater environment including rivers and lakes, the water bodies are usually turbid and dynamic, which brings extra troubles to quality of imaging due to color deviation and suspended particulate. Most of the existing underwater imaging methods focus on relatively clear underwater environment, it is uncertain that if those methods can work well in turbid and dynamic underwater environments. In this paper, we propose a turbidity-adaptive underwater image enhancement method. To deal with attenuation and scattering of varying degree, the turbidity is detected by the histogram of images. Based on the detection result, different image enhancement strategies are designed to deal with the problem of color deviation and blurring. The proposed method is verified by an underwater image dataset captured in real underwater environment. The result is evaluated by image metrics including structure similarity index measure, underwater color image quality evaluation metric, and speeded-up robust features. Test results exhibit that the method can correct the color deviation and improve the quality of underwater images. 相似文献
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11.
Zhenyu HU;Qi CHEN;Daqi ZHU 《光学精密工程》2022,30(17):2133-2146
This study proposes an underwater enhancement algorithm based on color balance and multi-scale fusion to address the color deviation, detail blur, and low contrast of underwater images caused by water absorbing and scattered light. A color balance method was used to correct color. Then, the color-corrected image was converted from the RGB space to Lab space, and the L-channel was processed with the contrast limited adaptive histogram equalization method to enhance the contrast. Subsequently, the image was converted back to the RGB space. Finally, the multi-scale fusion method was used to fuse the color-corrected image with the contrast-enhanced image according to weight maps. After image enhancement, the enhancement effect of the proposed algorithm was compared with that of other algorithms in terms of visual effect and image quality evaluations. Experiments show that the proposed algorithm can remove color deviation of an underwater image, as well as improve its clarity and contrast. Compared with the original image, the entropy, UIQM, and UCIQE of the processed image increase by at least 5.2%, 1.25 times, and 30.8%, respectively, thereby proving that the proposed algorithm can effectively improve the visual quality of underwater images. 相似文献
12.
JI Yuan;LI Xingyi;MA Xinde;LIAO Liang 《光学精密工程》2023,31(17):2573-2583
To address the problem of limited luminance dynamic range in stage scenes, this study proposes a method for low-light enhancement of the stage based on an improved Retinex algorithm. First, the low-light image of the stage scene is enhanced using the improved Retinex algorithm to obtain an overall enhanced image. Then, the original image is fused with the enhanced image, and background areas that are over-enhanced and do not need to be enhanced are processed to obtain the final image. The improved Retinex algorithm uses a Gauss-Laplace high-pass filter to find the reflected and illumination components, thus addressing the problem of detail loss in the reflection component. It then performs contrast and detail enhancement on the reflected component and multiplies it with the light component to produce the enhanced image. This method performs field-programable gate array （FPGA） hardware platform verification based on software platform verification. The experimental results show that, compared with other classical methods, this method yields a noticeable visual improvement, with an average increase of 57.06% in peak signal-to-noise ratio （PSNR） and 27.34% in structural similarity （SSIM） in different stage scenes. This improvement is particularly significant in stage scenes with substantial differences in brightness between light and dark areas. The images processed by this method restore the true luminance dynamic range of the stage and exhibit good natural color saturation without distortion, ensuring better image quality. 相似文献
13.
CHENG Deqiang;ZHANG Huaqiang;KOU Qiqi;LÜ Chen;QIAN Jiansheng 《光学精密工程》2023,31(20):2993-3009
Due to a high number of areas with low texture and lighting in complex indoor scenes, current self-supervised monocular depth estimation network models suffer from certain issues. These problems include imprecise depth predictions, noticeable blurriness around object edges in the predictions, and significant loss of details. This paper introduces an indoor self-supervised monocular depth estimation network model based on level feature fusion. First, to enhance the visibility of poorly lit areas and address the issue of pseudo planes deteriorating the model, the Mapping-Consistent Image Enhancement module was applied to process indoor images. This module simultaneously maintained brightness consistency. Subsequently, a novel self-supervised monocular depth estimation network model that incorporates the Cross-Level Feature Adjustment module was proposed, utilizing an attention mechanism. This module effectively fused multilevel feature information from the encoder to the decoder, enhancing the network's ability to utilize feature information and reducing the semantic gap between predicted depth and true depth. Finally, the Gram Matrix Similarity Loss function was introduced based on image style features, as an additional self-supervised signal to further constrain the network model. This addition enhanced the network’s depth prediction capabilities, leading to improved accuracy. Through training and testing on NYU Depth V2 and ScanNet indoor datasets, this paper achieves a pixel accuracy rate of 81.9% and 76.0%, respectively. The experimental results also include a comparative analysis with existing main indoor self-supervised monocular depth estimation network models. The network model proposed in this paper excels in preserving object edges and details, effectively enhancing the accuracy of predicted depth. 相似文献
14.
针对遥感图像成像过程中噪声污染严重,超分辨率重建图像存在目标边缘模糊和伪影等问题,本文提出一种融合边缘增强与非局部模块的遥感图像超分辨率算法（Edge-Enhanced and Non-local Modules Generative Adversarial Network,ENGAN）。为了使图像细节边缘更清晰,本文融合一种图像边缘增强模块;同时,为进一步扩大模型感受野和增强去除边缘噪声性能,改进边缘增强模块中的Mask分支;此外,引入非局部模块,通过更好地利用图像的内在特征相关性,进一步提升了网络的重建性能。本文在UCAS-AOD和NWPU VHR-10两种遥感图像数据集上进行多个算法的对比实验,结果表明本文提出的方法在多个评价指标上均有所改善。以退化类型Ⅳ为例,本文方法相比深度盲超分辨率退化模型,4倍超分辨率的SSIM提升了0.068,PSNR提升了1.400 dB,RMSE减少了12.5%,且重建后的遥感图像相较于原始图像可以得到更好的地面目标检测结果。 相似文献
15.
针对CCD图或红外热像图人脸识别方法的局限性,本文从多传感器图像融合技术出发,采用基于CCD图与红外热像图决策级融合的人脸识别方法,并建立了一个800幅的人脸图像库,进行人脸识别实验。实验表明：基于决策融合的人脸识别方法比单纯的CCD图或红外热像图人脸识别方法,具有更高的识别率。 相似文献
16.
对当前反求工程关键技术之一的特征提取技术进展作了综述．特征提取分为边界特征提取和过渡特征提取,前者分为基于点云的提取和基于三角网格的提取;后者分为等半径过渡特征提取和变半径过渡特征提取．分析和比较不同提取技术的优缺点,最后再介绍一种用于提取变半径过渡特征的多阈值精度比较法． 相似文献
17.
为避免线结构光测量中的干涉散斑噪声的影响,提出采用相干性低的 LED 光源的线结构光测量法,并搭建了测量系统。通过分析该方法在测量金属工件表面的条纹图像特点,提出了采用基于拉普拉斯金字塔的曝光图像融合方法来增强条纹图像质量。实验室环境图像的融合实验验证了该图像增强方法的可行性。最后完成了条纹图像的增强实验,结果表明,经 2 幅曝光图像融合后的条纹图的质量得到了提高,光条区的光强分布均匀,不存在明显的信息缺失区。 相似文献
18.
Xiangzhi Bai 《Microscopy research and technique》2013,76(2):163-172
To well enhance the mineral image and image details obtained by microscopes, an effective mineral image enhancement algorithm through feature extraction using the morphological center operator is proposed in this work. First, mineral image feature extraction based on the morphological center operator is proposed and discussed. Second, the multiscale extension of the mineral image feature extraction is given by using the multiscale structuring elements. Third, the important mineral image features at multiscales of image are extracted and used to construct the final mineral features for mineral image enhancement. Finally, the original mineral image is well enhanced through importing the extracted final mineral image features into the original mineral image. Experimental results on different types of microscopy images of minerals verified the effective performance of the proposed algorithm for microscopy mineral image enhancement. Microsc. Res. Tech., 2013. © 2012 Wiley Periodicals, Inc. 相似文献
19.
射线图像增强技术仿真研究 总被引：1，自引：0，他引：1
陈燕 《机械工程与自动化》2012,(2):56-57,60
图像增强在生物医学、无损检测、卫星遥感等领域得到广泛应用。对于低对比度射线图像而言,局部图像的反差较小是其特点。针对这一特点,将全局自适应均衡与局部动态增强相结合,通过增强倍数进行局部动态调整以适合各种对比度增强的要求。仿真结果表明,该算法能有效提高低对比度射线图像的整体对比度并同时突出细节。在实际应用中,对于其他成像技术的处理也有很好的参考价值。 相似文献
20.
提出了一种多通道特征融合学习的印制电路板小目标缺陷检测网络 YOLOPCB,首先删除 YOLOv7 主干网络中最后一组 MPConv 层与 E-ELAN 层,去掉融合层的 ECU 模块与 20×20 的预测头,使用跨通道信息连接模块串联精简后的主干和融合网络;其次设计了浅层特征融合模块与新的 anchors 匹配策略,增加了两个低层次、高分辨率检测头;最后将 YOLOv7 主干网络中的 3 个 E-ELAN 作为输入,将融合层中最底部的 E-ELAN 和两个拼接模块作为输出,使用自适应加权跳层连接以增加同维度内信息量。 在 PCB Defect 公开数据集上平均精度达到 94. 9% ,检测速度达到 45. 6 fps;最后在企业现场制作的Self-PCB 数据集中,YOLOPCB 达到了最高精度 76. 7% ,比 YOLOv7 检测精度提升了 6. 8% ,能有效提高印制电路板小目标缺陷检测能力。 相似文献