import cv2
import numpy as np

# 读取低分辨率图像
lr_img = cv2.imread('low_res_image.tif', cv2.IMREAD_UNCHANGED)

# 双三次插值
bicubic_img = cv2.resize(lr_img, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)

# 保存结果
cv2.imwrite('bicubic_upscaled.tif', bicubic_img)

# 加载预训练的EDSR模型
# 需要先下载模型文件(.pb)
net = cv2.dnn.readNetFromTensorflow('EDSR_x2.pb')

# 预处理输入图像
blob = cv2.dnn.blobFromImage(lr_img, 1.0, (lr_img.shape[1], lr_img.shape[0]), 
                            (0, 0, 0), swapRB=False, crop=False)

# 执行超分
net.setInput(blob)
sr_img = net.forward()

# 后处理
sr_img = np.clip(sr_img, 0, 255)
sr_img = sr_img.squeeze().transpose(1, 2, 0)

from tensorflow.keras.models import load_model

# 加载预训练模型(如ESPCN, SRCNN等)
model = load_model('srcnn_model.h5')

# 预处理
lr_img = lr_img.astype('float32') / 255.0
lr_img = np.expand_dims(lr_img, axis=0)

# 预测
sr_img = model.predict(lr_img)

# 后处理
sr_img = np.clip(sr_img[0] * 255, 0, 255).astype('uint8')

# 示例代码框架 - 需要具体实现或加载预训练模型
from keras.layers import Conv2D, PReLU, Add

def build_rssrcnn():
    # 构建遥感专用SRCNN模型
    model = Sequential()
    # 添加遥感影像特定的层结构
    # ...
    return model

model = build_rssrcnn()
model.load_weights('rssrcnn_weights.h5')

# 例如RCAN (Residual Channel Attention Network)
from model.rcan import RCAN

model = RCAN(scale=2, num_rcab=10)
model.load_weights('rcan_remote_sensing.h5')

import gdal
import numpy as np
from skimage.metrics import peak_signal_noise_ratio as psnr

def process_remote_sensing_sr(input_path, output_path, scale=2, method='srcnn'):
    # 读取遥感影像
    dataset = gdal.Open(input_path)
    img = dataset.ReadAsArray()

    # 归一化
    img = img.astype('float32') / np.max(img)

    # 根据选择的方法处理
    if method == 'bicubic':
        sr_img = cv2.resize(img.transpose(1, 2, 0), None, fx=scale, fy=scale, 
                           interpolation=cv2.INTER_CUBIC)
    elif method == 'srcnn':
        # 使用SRCNN模型预测
        pass

    # 保存结果
    driver = gdal.GetDriverByName('GTiff')
    out_dataset = driver.Create(output_path, sr_img.shape[1], sr_img.shape[0],
                               sr_img.shape[2], gdal.GDT_Float32)
    # 设置地理信息等...

    # 评估
    if has_ground_truth:
        gt = gdal.Open(gt_path).ReadAsArray()
        print(f"PSNR: {psnr(gt, sr_img)}")

    return sr_img