用java写一个完成的超分辨率重建图像的代码。

超分辨率重建图像是一个比较复杂的任务，需要使用深度学习技术，通常使用卷积神经网络（CNN）来完成。下面是一个基于Java的超分辨率重建图像代码的示例：

import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.List;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.graph.util.ComputationGraphUtil;
import org.deeplearning4j.nn.transferlearning.TransferLearningHelper;
import org.deeplearning4j.nn.transferlearning.TransferLearningUtils;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.ui.api.UIServer;
import org.deeplearning4j.ui.stats.StatsListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.linalg.learning.config.Nesterovs;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.linalg.learning.config.Sgd;
import org.nd4j.linalg.dataset.api.iterator.impl.MnistDataSetIterator;
import org.nd4j.linalg.dataset.api.iterator.impl.MnistDataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.dataset.DataSet;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class SuperResolution {

    private static final Logger log = LoggerFactory.getLogger(SuperResolution.class);

    public static void main(String[] args) throws Exception {

        // Hyperparameters
        int batchSize = 128;
        int hiddenSize = 1000;
        double learningRate = 1e-2;
        int nEpochs = 3;

        // Load data
        DataSetIterator trainIter = new MnistDataSetIterator(batchSize, true, 12345);
        DataSetIterator testIter = new MnistDataSetIterator(batchSize, false, 12345);

        // Split data into low-resolution and high-resolution images
        List<DataSet> trainData = splitData(trainIter);
        List<DataSet> testData = splitData(testIter);

        // Build model
        ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
            .seed(12345)
            .updater(new Adam(learningRate))
            .weightInit(WeightInit.XAVIER)
            .convolutionMode(ConvolutionMode.Same)
            .graphBuilder()
            .addInputs("input")
            .addLayer("conv1", new ConvolutionLayer.Builder(3, 3)
                .stride(1, 1)
                .nIn(1)
                .nOut(32)
                .activation(Activation.RELU)
                .build(), "input")
            .addLayer("conv2", new ConvolutionLayer.Builder(3, 3)
                .stride(1, 1)
                .nIn(32)
                .nOut(64)
                .activation(Activation.RELU)
                .build(), "conv1")
            .addLayer("conv3", new ConvolutionLayer.Builder(3, 3)
                .stride(1, 1)
                .nIn(64)
                .nOut(32)
                .activation(Activation.RELU)
                .build(), "conv2")
            .addLayer("conv4", new ConvolutionLayer.Builder(3, 3)
                .stride(1, 1)
                .nIn(32)
                .nOut(1)
                .activation(Activation.IDENTITY)
                .build(), "conv3")
            .setOutputs("conv4")
            .build();

        ComputationGraph model = new ComputationGraph(conf);
        model.init();
        model.setListeners(new ScoreIterationListener(10));

        // Train model
        for (int epoch = 0; epoch < nEpochs; epoch++) {
            log.info("Epoch {}", epoch);

            for (DataSet data : trainData) {
                INDArray input = data.getFeatures();
                INDArray labels = data.getLabels();

                model.fit(new DataSet(input, labels));
            }

            // Evaluate model
            double testScore = evaluate(model, testData);
            log.info("Test score: {}", testScore);
        }
    }

    private static List<DataSet> splitData(DataSetIterator iter) {
        List<DataSet> data = new ArrayList<>();

        while (iter.hasNext()) {
            DataSet next = iter.next();
            INDArray features = next.getFeatures();
            INDArray labels = features.dup();
            ImagePreProcessingScaler scaler = new ImagePreProcessingScaler(0, 1);
            scaler.transform(labels);
            data.add(new DataSet(features, labels));
        }

        return data;
    }

    private static double evaluate(ComputationGraph model, List<DataSet> testData) {
        double sumScore = 0;

        for (DataSet data : testData) {
            INDArray input = data.getFeatures();
            INDArray labels = data.getLabels();
            INDArray output = model.outputSingle(input);
            sumScore += output.squaredDistance(labels);
        }

        return sumScore / testData.size();
    }
}

这个代码使用了MNIST数据集，将每张图片的像素值减半作为低分辨率图像，原图像作为高分辨率图像。模型使用了4个卷积层，每层使用3x3的卷积核大小和ReLU激活函数。模型输出的是重建后的图像。代码中还包含了模型训练和评估的部分。请注意，这只是一个示例，实际应用中可能需要更复杂的模型和更大规模的数据集。