基于深度学习的超分辨率重建python代码复现

超分辨率重建是一种将低分辨率图像转换为高分辨率图像的技术。在深度学习领域，超分辨率重建主要基于卷积神经网络（CNN）实现。

以下是一份基于Python的超分辨率重建代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Input, Lambda
from tensorflow.keras.models import Model
import numpy as np
import cv2

def build_model():
    # 定义输入层
    input_layer = Input(shape=(None, None, 3))
    
    # 定义卷积层
    conv_layer1 = Conv2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu')(input_layer)
    conv_layer2 = Conv2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu')(conv_layer1)
    
    # 定义残差块
    residual_layer = conv_layer2
    for i in range(16):
        residual_layer = Conv2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu')(residual_layer)
    residual_layer = Conv2D(filters=64, kernel_size=3, strides=1, padding='same')(residual_layer)
    output_layer = Lambda(lambda x:x*0.1)(residual_layer)
    
    # 定义模型
    model = Model(inputs=[input_layer], outputs=[output_layer])
    return model

def load_image(filepath):
    img = cv2.imread(filepath)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = img / 255.0
    return img

def save_image(img, filename):
    img = np.clip(img, 0, 1)
    img = img * 255.0
    img = img.astype(np.uint8)
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    cv2.imwrite(filename, img)

model = build_model()
model.load_weights('model_weights.h5')

input_image = load_image('input_image.jpg')
input_image = np.expand_dims(input_image, axis=0)
output_image = model.predict(input_image)
output_image = np.squeeze(output_image, axis=0)
save_image(output_image, 'output_image.jpg')

这份代码使用了TensorFlow和OpenCV库。首先，我们定义了一个基于CNN的超分辨率重建模型。然后，我们加载预训练好的模型权重，并使用OpenCV库读取输入图像。接着，我们将输入图像传入模型中进行超分辨率重建，并将输出图像保存到本地。