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基于深度卷积神经网络图像去噪算法

于 2020-10-15 发布
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下载积分: 1 下载次数: 6
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代码说明：

说明：  用于图像去噪处理，使用ADM方法图像去噪处理器处理(Used for image denoising processing, using adm method image denoising processor processing)

文件列表：

DnCNN-Denoise-Gaussian-noise-TensorFlow-master, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\DnCNN.py, 2950 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised1.jpg, 9243 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised2.jpg, 6945 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised3.jpg, 9026 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised4.jpg, 11065 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised5.jpg, 11102 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised6.jpg, 9139 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\denoised7.jpg, 10044 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\method.jpg, 39256 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised1.jpg, 20627 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised2.jpg, 17248 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised3.jpg, 18682 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised4.jpg, 20420 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised5.jpg, 19110 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised6.jpg, 20174 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\IMAGES\noised7.jpg, 21734 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\LICENSE, 1067 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\README.md, 3367 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\01.png, 38267 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\02.png, 34985 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\03.png, 40181 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\04.png, 42947 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\05.png, 40728 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\06.png, 40985 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\07.png, 39804 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\08.png, 151065 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\09.png, 185727 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\10.png, 177762 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\11.png, 209817 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TestingSet\12.png, 193637 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingResults, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingResults\0_1440.jpg, 1847 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingResults\0_1520.jpg, 1830 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingResults\0_1600.jpg, 2277 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_17.jpg, 674 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_18.jpg, 619 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_19.jpg, 648 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_20.jpg, 579 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_25.jpg, 665 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_26.jpg, 677 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_27.jpg, 640 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\TrainingSet\1_28.jpg, 611 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\config.py, 106 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\network.py, 557 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\ops.py, 4376 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\save_para, 0 , 2019-03-02
DnCNN-Denoise-Gaussian-noise-TensorFlow-master\save_para\READMEN.txt, 30 , 2019-03-02

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  • retinex_frankle_mccann
    Retinex算法对较暗区域的图像处 理有明显效果,改进的Retinex方法则是基于经典Retinex,对具有较高亮度的遥感图像通过图像变 换方法达到去除模糊的目的。 (Retinex algorithm for image processing on the dark region was effective to improve the Retinex method is based on the classic Retinex, on remote sensing images with high brightness achieved by an image transformation method aims to remove ambiguity.)
    2010-06-10 16:24:00下载
    积分：1
  • superresolution_v_2[1].0
    说明：  图像超分辨是目前国内外研究的热点，该算法主要用于光学图像的超分辨(Image super-resolution is the study of hot spots at home and abroad that the algorithm is mainly used in optical image of the super-resolution)
    2008-10-04 19:55:35下载
    积分：1
  • NSCT
    彩色图像融合 NSCT 实现红外与可见光图像融合(Color image fusion NSCT realize infrared and visible image fusion)
    2014-12-25 16:11:03下载
    积分：1
  • gangjingshushu
    能完成钢筋数数，图像分割，直线检测，边缘检测，很好用。(Can finish the reinforcing bar counting, image segmentation, straight line detection, edge detection and works well.)
    2020-11-04 15:19:51下载
    积分：1
  • 合成孔径雷达（SAR）的距离多普勒成像算法（点目标） range_doppler
    合成孔径雷达（SAR）的距离多普勒成像算法（点目标）(Synthetic aperture radar (SAR) of Range-Doppler imaging algorithm (point target))
    2008-05-06 17:10:57下载
    积分：1
  • 20081115
    说明：  数学形态学的腐蚀、膨胀和细化等操作，包含.exe文件(Mathematical Morphology corrosion, expansion and refinement, such as operations, contains. Exe files)
    2008-11-16 19:31:48下载
    积分：1
  • activeContoursDemo
    图像轮廓提取，Snakes方法来提取CT图像的外轮廓(Snakes method)
    2015-05-04 21:23:13下载
    积分：1
  • RRT-path-panning-algorithm
    本文以自主驾驶车辆为实际应用背景．提出了一种改进的ＲＲＴ（快速随机搜索树）路径规划算法．该路 径规划算法将非完整性约束条件与双向多步扩展ＲＲＴ搜索算法相结合，在提高搜索效率的同时保证了规划路径的可 行性．同时将路径点作为Ｂ样条基函数的控制点，用三次Ｂ样条函数来拟合控制点生成平滑可跟踪的路径．通过在平 面障碍物环境下实验，验证了该算法的有效性．(ＡｉｍｉｎｇａｔｔｈｅａｐｐｌｉｃａｔｉｏｎｏｆＡＬＶ（ａｕｔｏｍａｔｉｃｌａｎｄｖｅｈｉｃｌｅ），ａｎｉｍｐｒｏｖｅｄＲＲＴ（ＲａｐｉｄｌｙＲａｎｄｏｍ?ｅｘｐｌｏｒｉｎｇ Ｔｒｅｅｓ）ｐａｔｈｐｌａｎｎｉｎｇａｌｇｏｒｉｔｈｍ ｉｓｐｒｏｐｏｓｅｄ．Ｔｈｅａｌｇｏｒｉｔｈｍ ｃｏｍｂｉｎｅｓｔｈｅｎｏｈｏｌｏｎｏｍｉｃｃｏｎｓｔｒａｉｎｔｓｏｆｖｅｈｉｃｌｅｗｉｔｈｄｏｕｂｌｅｅｘｔｅｎｄ ＲＲＴｓ，ｗｈｉｃｈｎｏｔｏｎｌｙｉｍｐｒｏｖｅｓｔｈｅｅｆｆｉｃｉｅｎｃｙｏｆｓｅａｒｃｈｉｎｇｂｕｔａｌｓｏｇｕａｒａｎｔｅｅｓｔｈｅｆｅａｓｉｂｉｌｉｔｙｏｆｔｈｅｐａｔｈａｔｔｈｅｓａｍｅｔｉｍｅ．Ｔｈｅｐａｔｈ ｐｏｉｎｔｓａｒｅｔａｋｅｎａｓｒｅｆｅｒｅｎｃｅｐｏｉｎｔｓｆｏｒｔｈｅＢ?ｓｐｌｉｎｅｂａｓｉｃｆｕｎｃｔｉｏｎ，ａｐｐｒｏａｃｈａｎｄｃｒｅａｔｅａｎｅｗｓｍｏｏｔｈｒｏｕｔｅｗｈｉｃｈｓｕｉｔｓｆｏｒｔｈｅｖｅｈｉ? ｃｌｅｔｏｆｏｌｌｏｗ．Ｔｈｅｓｉｍｕｌａｔｉｏｎｏｕｔｃｏｍｅｓａｎｄｆｉｅｌｄｔｅｓｔｓｖｅｒｉｆｙｔｈｅｅｆｆｅｃｔｉｖｅｎｅｓｓｏｆｔｈｅｍｅｔｈｏｄｐｒｏｐｏｓｅｄ．)
    2012-12-01 18:39:27下载
    积分：1
  • image_processing
    本程序是基于Matlab的米粒图像的分割、计数以及周长、面积、形状因子等参数的计算。图像分割是基于腐蚀膨胀算法，选用边界跟踪法计算周长，程序代码清晰明了，对于参数的计算速度较快，且参数计算精度较高。(This procedure is based on the Matlab image segmentation grain of rice, counting and perimeter, area, shape factor and other parameters of the calculation. Image segmentation is based on the corrosion expansion algorithm, the choice of boundary tracking method perimeter, code clarity, the parameter calculation is faster and higher precision parameters.)
    2010-12-20 17:45:09下载
    积分：1
  • bg_comp
    B样条曲面方法，通过b样条曲面的拟合得到图像的背景，减去背景图像就能得到重要内容的图像，除去了背景对图像内容的干扰，也可解决光照不均的问题(algorithms for cytoplasm segmentation of fluorescence labelled cells)
    2012-03-19 10:45:33下载
    积分：1
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