应用中值滤波器和维纳滤波器

应用中值滤波器和维纳滤波器

中值滤波器是一种非线性数字滤波技术，通常用于去除噪声。这种噪声减少是用于改善后续处理结果的典型预处理步骤。维纳滤波器是针对受到附加噪声和模糊影响的图像而言的均方误差（MSE）最佳稳态线性滤波器。使用Aspose.PSD for Java API，开发人员可以对图像应用中值滤波器进行去噪，并可以对图像应用高斯维纳滤波器。本文演示了如何将中值滤波器和高斯维纳滤波器应用于图像。

应用中值滤波器

Aspose.PSD提供了MedianFilterOptions类，用于在RasterImage上应用滤波器。下面提供的代码片段演示了如何将中值滤波器应用于光栅图像。

        String dataDir = Utils.getDataDir(ApplyMedianAndWienerFilters.class) + "Conversion/";

        String sourceFile = dataDir + "sample.psd";
        String destName = dataDir + "median_test_denoise_out.gif";

        try (Image image = Image.load(sourceFile);
// Cast the image into RasterImage
             RasterImage rasterImage = (RasterImage) image) {
            if (rasterImage == null) {
                return;
            }

            // Create an instance of MedianFilterOptions class and set the size, Apply MedianFilterOptions filter to RasterImage object and Save the resultant image
            MedianFilterOptions options = new MedianFilterOptions(4);
            rasterImage.filter(image.getBounds(), options);
            image.save(destName, new GifOptions());
        }

应用高斯维纳滤波器

Aspose.PSD提供了GaussWienerFilterOptions类，用于在RasterImage上应用滤波器。下面提供的代码片段演示了如何将高斯维纳滤波器应用于光栅图像。

        String dataDir = Utils.getDataDir(ApplyGausWienerFilters.class) + "Conversion/";

        String sourceFile = dataDir + "sample.psd";
        String destName = dataDir + "gauss_wiener_out.gif";

        try (Image image = Image.load(sourceFile);
             RasterImage rasterImage = (RasterImage) image) {
            if (rasterImage == null) {
                return;
            }
// Create an instance of GaussWienerFilterOptions class and set the radius size and smooth value.
            GaussWienerFilterOptions options = new GaussWienerFilterOptions(12, 3);
            options.setGrayscale(true);

            // Apply MedianFilterOptions filter to RasterImage object and Save the resultant image
            rasterImage.filter(image.getBounds(), options);

            image.save(destName, new GifOptions());
        }

应用有色图像的高斯维纳滤波器

Aspose.PSD还为彩色图像提供了GaussWienerFilterOptions。下面提供的代码片段演示了如何将高斯维纳滤波器应用于彩色图像。

        String dataDir = Utils.getDataDir(ApplyGausWienerFiltersForColorImage.class) + "Conversion/";
        String sourceFile = dataDir + "sample.psd";
        String destName = dataDir + "gauss_wiener_color_out.gif";

        try (Image image = Image.load(sourceFile);
             // Cast the image into RasterImage
             RasterImage rasterImage = (RasterImage) image) {
            if (rasterImage == null) {
                return;
            }

            // Create an instance of GaussWienerFilterOptions class and set the radius size and smooth value.
            GaussWienerFilterOptions options = new GaussWienerFilterOptions(5, 1.5);
            options.setBrightness(1);

            // Apply MedianFilterOptions filter to RasterImage object and Save the resultant image
            rasterImage.filter(image.getBounds(), options);
            image.save(destName, new GifOptions());
        }

应用运动维纳滤波器

Aspose.PSD提供了MotionWienerFilterOptions类，用于在RasterImage上应用滤波器。下面提供的代码片段演示了如何将运动维纳滤波器应用于光栅图像。

        String dataDir = Utils.getDataDir(ApplyMotionWienerFilters.class) + "Conversion/";

        String sourceFile = dataDir + "sample.psd";
        String destName = dataDir + "motion_filter_out.gif";

        try (Image image = Image.load(sourceFile);
// Cast the image into RasterImage
             RasterImage rasterImage = (RasterImage) image) {
            if (rasterImage == null) {
                return;
            }

            // Create an instance of MotionWienerFilterOptions class and set the length, smooth value and angle.
            MotionWienerFilterOptions options = new MotionWienerFilterOptions(50, 9, 90);
            options.setGrayscale(true);

            // Apply MedianFilterOptions filter to RasterImage object and  Save the resultant image
            rasterImage.filter(image.getBounds(), options);
            image.save(destName, new GifOptions());
        }

对图像应用校正滤波器

本文演示了使用Aspose.PSD for Java对图像执行校正滤波器的用法。Aspose.PSD API提供了高效且易于使用的方法来实现这一目标。Aspose.PSD for Java为滤波提供了BilateralSmoothingFilterOptions和SharpenFilterOptions类。BilateralSmoothingFilterOptions类需要一个整数作为大小。执行调整尺寸的步骤如下：

1. 使用Image类公开的Load工厂方法加载图像。
2. 将图像转换为RasterImage。
3. 创建BilateralSmoothingFilterOptions和SharpenFilterOptions类的实例。
4. 调用RasterImage.Filter方法，同时指定矩形作为图像边界和BilateralSmoothingFilterOptions类实例。
5. 调用RasterImage.Filter方法，同时指定矩形作为图像边界和SharpenFilterOptions类实例。
6. 调整对比度
7. 设置亮度
8. 保存结果。

以下代码片段显示了如何应用校正滤波器。

        try (Image image = Image.load(dataDir + "sample.psd");
             // Convert the image into RasterImage.
             RasterImage rasterImage = (RasterImage) image) {
            if (rasterImage == null) {
                return;
            }
            // Get Bounds[rectangle] of image.
            Rectangle rect = image.getBounds();

            // Create an instance of BilateralSmoothingFilterOptions class with size
            // parameter.
            BilateralSmoothingFilterOptions bilateralOptions = new BilateralSmoothingFilterOptions(3);

            // Create an instance of SharpenFilterOptions class.
            SharpenFilterOptions sharpenOptions = new SharpenFilterOptions();

            // Supply the filters to raster image.
            rasterImage.filter(rect, bilateralOptions);
            rasterImage.filter(rect, sharpenOptions);

            // Adjust the contrast accordingly.
            rasterImage.adjustContrast(-10);

            // Set brightness using Binarize Bradley
            rasterImage.binarizeBradley(80);

            // Save the results to output path.
            rasterImage.save(dataDir + "a1_out.jpg");
        }

使用Bradley阈值算法

图像阈值化在图形应用程序中有所应用。图像阈值化的目标是将像素分类为“暗”或“亮”。Aspose.PSD API允许您在转换图像时使用Bradley阈值化。以下代码片段演示了如何定义阈值并调用Bradley的阈值算法。

        String dataDir = Utils.getDataDir(Bradleythreshold.class) + "Conversion/";
        String sourceFile = dataDir + "sample.psd";
        String destName = dataDir + "binarized_out.png";

// Load an image
        try (PsdImage image = (PsdImage) Image.load(sourceFile)) {
// Define threshold value, Call BinarizeBradley method and pass the threshold value as parameter and Save the output image
            double threshold = 0.15;
            image.binarizeBradley(threshold);
            image.save(destName, new PngOptions());
        }