목록DE (34)
UOMOP
Cifar10 Fourier
import matplotlib.pyplot as pltimport numpy as npimport torchvision.transforms as transformsimport torchvision.datasets as datasetsfrom torch.utils.data import DataLoaderfrom scipy.ndimage import sobel# CIFAR-10 데이터셋 로드transform = transforms.Compose([transforms.ToTensor()])cifar10 = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)dataloader = DataLoader(cifar10, ba..
Avg PSNR, Canny, Sobel, Fourier
import matplotlib.pyplot as pltfrom sklearn.preprocessing import MinMaxScalerimport pandas as pd# 엑셀 파일 경로 (실제 경로로 수정)file_path = 'C:/Users/dowon/Desktop/CISL/DE/CBS(Lv4)/Table_DIM_1024_SNR_40.xlsx'# 엑셀 파일 불러오기df = pd.read_excel(file_path)# 그래프를 그리기 위한 필요한 코드들columns_to_plot = ['Avg PSNR', 'Canny Edge', 'Sobel Edge', 'Fourier Transform']data_to_plot = df[columns_to_plot]# 데이터 0~1로 스케일링scaler = M..
Performance graph
% 데이터 입력 (DIM 512)SNR = [40, 30, 20, 10, 0];deep = [30.021, 29.227, 26.817, 22.801, 18.781];proposed_Lv1 = [28.866, 28.233, 25.812, 23.188, 18.894];proposed_Lv2 = [27.473, 27.007, 25.179, 22.885, 19.197];proposed_Lv3 = [26.519, 25.207, 24.152, 21.745, 19.025];proposed_Lv4 = [25.059, 25.141, 23.567, 21.731, 17.874];random_Lv1 = [28.199, 27.794, 25.53, 22.885, 18.807];random_Lv2 = [25.077,..
ProposedDIM:256 SNR(40dB) : (Avg):25.442 (Ori.):25.945 (MR=33%):25.107 (MR=65%):24.548 (MR=75%):23.362 DIM:256 SNR(30dB) : (Avg):24.807 (Ori.):25.630 (MR=33%):24.855 (MR=65%):24.330 (MR=75%):23.221 DIM:256 SNR(20dB) : (Avg):23.393 (Ori.):24.138 (MR=33%):23.646 (MR=65%):23.257 (MR=75%):22.419 DIM:256 SNR(10dB) : (Avg):21.857 (Ori.):21.506 (MR=33%):21.461 (MR=65%):21.058 (MR=75%):20.533 DIM:256 SN..
DE dim = 256
% 데이터 입력SNR = [40, 30, 20, 10, 0];deep = [26.248, 25.997, 24.505, 21.318, 17.638];Ori = [25.945, 25.63, 24.138, 21.506, 17.799];MR_33 = [25.107, 24.855, 23.646, 21.461, 18.165];MR_65 = [24.548, 24.33, 23.257, 21.058, 17.79];MR_75 = [23.362, 23.221, 22.419, 20.533, 17.56];% 그래프 그리기figure;hold on;plot(SNR, Ori, '-s', 'DisplayName', 'Lv.1 transmission (Original image)', 'LineWidth', 2);plot(SNR, MR..
Dynamic encoder performance
% 데이터 입력SNR = [40, 30, 20, 10, 0];deep = [30.021, 29.227, 26.817, 22.801, 18.781];Ori = [28.866, 28.233, 25.812, 23.185, 18.894];MR_33 = [27.473, 27.001, 25.179, 22.885, 19.197];MR_65 = [25.509, 25.141, 23.906, 21.745, 17.84];MR_75 = [23.894, 23.696, 22.952, 21.731, 18.413];% 그래프 그리기figure;hold on;plot(SNR, Ori, '-s', 'DisplayName', 'Lv.1 transmission (Original image)', 'LineWidth', 2);plot(SNR,..