Laplacian Pyramid 是一种非常强大的图像多尺度分析工具,它能够将图像分解为不同尺度的子图像,为图像融合、增强、压缩等任务提供了有力的支持。通过理解 Laplacian Pyramid 的原理和构建方法,我们可以更好地应用它来解决各种图像处理问题。在未来的计算机视觉和图像处理领域,Laplacian Pyramid 有望继续发挥重要的作用。
Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals Minsoo Song, Seokjae Lim, Wonjun Kim 8 Jan 2021 322 SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models Weihua Chen, Fan Wang, Zhaoyang Sun, Fei Du, Yi Rong, Shengwu Xiong, Yaxiong Chen ...
def loadPyramid(source: str, prefix="first") -> Tuple[List[np.ndarray]]: global resource_path, DEBUG img = cv2.imread(path.join(resource_path, source)) if img.shape[0] < 256 or img.shape[1] < 256: raise "the shape of image must be greater than (256,256)" img = img[:256,...
buildGaussianPyramid(); } void buildGaussianPyramid() {//金字塔内容为每一层的掩模 assert(leftLapPyr.size()>0); maskGaussianPyramid.clear(); Mat currentImg; cvtColor(blendMask, currentImg, CV_GRAY2BGR);//store color img of blend mask into maskGaussianPyramid maskGaussianPyramid.push_back(curre...
Multi-level Laplacian Pyramid Reconstruction 结构:使用multiplicative gating来更有效地整合底层位置信息和高层语义信息 Reconstruction 对低分辨率的class score直接上采样可能忽略了一些编码在低分率feature map 通道之间的位置信息,所以文章地改进是使用更多的channels,相当于对每个类别对应的通道进行了K倍的扩充。
上4图为高斯金字塔,下4图为拉普拉斯金字塔。步骤: 1.上采样:每隔一个点补0或本身 2.作差:上采样的高斯图与上级高斯图相减 L(i)=g(i)-g(i+1).expand *应用:图像重构(需要L(1),…L(n)和g(n+1)) g(3)=L(3)+g(4).expand; g(2)=L(2)+g(3).expand; g(1)=L(1)+g(2).expand; ...
In its essence the Laplacian pyramid (Burt and Adelson, 1983) performs a dyadic tree-like spectral or subband analysis. The idea will be explained with a ID example and the link to a dyadic PR subband tree established here. In Fig. 3.29 the signal x(n) is low-pass filtered and decimate...
open_system(modelname); This model reads an input image using anImage From Fileblock with theFile nameparameter set tocameraman.tifandOutput data typeparameter set tosingle. To construct a Laplacian pyramid theResizeblock resizes the input image to 253-by-253 pixels, and performs reduce and exp...
The basic intuition for our multi-resolution architecture comes from Burt and Adelson’s classic Laplacian Pyramid [1], which decomposes an image into disjoint frequency bands using an elegant recursive computation (analysis) that produces appropriately down-sampled sub-bands such that the sum of the...
(dense_feat)# Dense feature for lev 6# decoder 1 - Pyramid level 6lap_lv6=torch.sigmoid(self.decoder1(dense_feat))# decoder就是将通道数逐渐缩减为1,表示depth maplap_lv6_up=self.upscale(lap_lv6,scale_factor=2,mode='bilinear')# decoder 2 - Pyramid level 5dec2=self.decoder2_up1(...