损失函数:MS-SSIM[1] 用于衡量两幅图像之间的差距。公式如下: importtorchimporttorch.nn.functionalasFfrommathimportexpimportnumpyasnp# 计算一维的高斯分布向量defgaussian(window_size,sigma):gauss=torch.Tensor([exp(-(x-window_size//2)**2/float(2*sigma**2))forxinrange(window_size)])returngauss/ga...
MS-SSIM: MS-SSIM(Multi-Scale Structural Similarity Index)是一种用于评估图像质量的指标,它是结构相似性指数(SSIM)在多个尺度上的扩展。 SSIM是一种衡量两幅图像相似性的指标,它考虑了图像的亮度、对比度和结构等方面。而MS-SSIM在SSIM的基础上引入了多个尺度,以更好地捕捉图像的细节信息。 具体而言,MS-SSIM...
SSIM(Structural SIMilarity)即结构相似性指数,是一种测量两个图像之间相似性的方法 假定其中一幅图像具有完美的质量,则 SSIM 指数可以被视为另一幅图像质量的度量。 SSIM 指数的计算流程如下图所示: 由SSIM 测量系统可得相似度的测量可由三种对比模块组成,分别为:亮度(l),对比度(c),结构(s),各个模块的计算公...
(170.7676 ms), ssim_torch=0.515869 (189.0941 ms) sigma=50.0 ssim_skimage=0.422551 (222.6846 ms), ssim_tf=0.422558 (273.1971 ms), ssim_paddle=0.422542 (168.3579 ms), ssim_torch=0.422554 (176.7442 ms) sigma=60.0 ssim_skimage=0.351337 (215.1536 ms), ssim_tf=0.351340 (270.5560 ms), ssim_...
sigma=90.0 ssim_skimage=0.219240 (224.7329 ms), ssim_tf=0.219245 (270.3727 ms), ssim_paddle=0.219235 (172.3580 ms), ssim_torch=0.219242 (180.5838 ms) sigma=100.0 ssim_skimage=0.192630 (238.8582 ms), ssim_tf=0.192634 (261.4317 ms), ssim_paddle=0.192624 (166.0294 ms), ssim_torch=0.192632 (17...
sigma=2.000000 compare_ssim=0.966521 (485.862017 ms) ssim_torch=0.966520 (237.199068 ms) sigma=3.000000 compare_ssim=0.928799 (323.492050 ms) ssim_torch=0.928797 (148.905993 ms) sigma=4.000000 compare_ssim=0.882271 (290.801048 ms) ssim_torch=0.882267 (146.914005 ms) ...
importpytorch_msssimimporttorchdevice=torch.device('cuda'iftorch.cuda.is_available()else'cpu')m=pytorch_msssim.MSSSIM()img1=torch.rand(1,1,256,256)img2=torch.rand(1,1,256,256)print(pytorch_msssim.msssim(img1,img2))print(m(img1,img2)) ...
MKFMIKUmentioned this issueJun 28, 2019 add ssim and ms-ssim loss #6934#22289 Closed veritas9872mentioned this issueAug 12, 2019 soupaultmentioned this issueMay 2, 2020 win_size exceeds image extent. If the input is a multichannel (color) image, set multichannel=True.scikit-image/scikit-im...
(usually 1.0 or 255) size_average (bool, optional): if size_average=True, ssim of all images will be averaged as a scalar win_size: (int, optional): the size of gauss kernel win_sigma: (float, optional): sigma of normal distribution win (torch.Tensor, optional): 1-D gauss kernel....
( img1, img2,window, data_range=255, size_average=True, window_size=11, channel=3, sigma=1.5, weights=None, C=(0.01, 0.03) ): r""" interface of ms-ssim Args: img1 (torch.Tensor): a batch of images, (N,C,[T,]H,W) img2 (torch.Tensor): a batch of images, (N,C,[T...