Java实现高斯模糊和图像的空间卷积

tianya1983 3年前
   <h2>高斯模糊</h2>    <p>高斯模糊(英语:Gaussian Blur),也叫高斯平滑,是在Adobe Photoshop、GIMP以及Paint.NET等图像处理软件中广泛使用的处理效果,通常用它来减少图像杂讯以及降低细节层次。这种模糊技术生成的图像,其视觉效果就像是经过一个半透明屏幕在观察图像,这与镜头焦外成像效果散景以及普通照明阴影中的效果都明显不同。高斯平滑也用于计算机视觉算法中的预先处理阶段,以增强图像在不同比例大小下的图像效果。 从数学的角度来看,图像的高斯模糊过程就是图像与正态分布做卷积。由于正态分布又叫作 <em>高斯分布</em> ,所以这项技术就叫作高斯模糊。图像与圆形方框模糊做卷积将会生成更加精确的焦外成像效果。由于高斯函数的傅立叶变换是另外一个高斯函数,所以高斯模糊对于图像来说就是一个低通滤波器。</p>    <p>高斯模糊运用了高斯的正态分布的密度函数,计算图像中每个像素的变换。</p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/3b954136082ea22d46ad2d7cadf2a366.png"></p>    <p style="text-align:center">gaussian-function.png</p>    <p>根据一维高斯函数,可以推导得到二维高斯函数:</p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/c114084b3ad9cda202a184e44331a9a6.png"></p>    <p style="text-align:center">二维高斯函数.png</p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/d8e524198f8271d45a56e7a044d19887.png"></p>    <p style="text-align:center">二维的正太分布.png</p>    <p>其中 <em>r</em> 是模糊半径,r^2 = x^2 + y^2,σ是正态分布的标准偏差。在二维空间中,这个公式生成的曲面的等高线是从中心开始呈正态分布的同心圆。分布不为零的像素组成的卷积矩阵与原始图像做变换。每个像素的值都是周围相邻像素值的加权平均。原始像素的值有最大的高斯分布值,所以有最大的权重,相邻像素随着距离原始像素越来越远,其权重也越来越小。这样进行模糊处理比其它的均衡模糊滤波器更高地保留了边缘效果。</p>    <p>其实,在iOS上实现高斯模糊是件很容易的事儿。早在iOS 5.0就有了Core Image的API,而且在CoreImage.framework库中,提供了大量的滤镜实现。</p>    <pre>  <code class="language-java">+(UIImage *)coreBlurImage:(UIImage *)image withBlurNumber:(CGFloat)blur   {       CIContext *context = [CIContext contextWithOptions:nil];       CIImage *inputImage= [CIImage imageWithCGImage:image.CGImage];       //设置filter      CIFilter *filter = [CIFilter filterWithName:@"CIGaussianBlur"];       [filter setValue:inputImage forKey:kCIInputImageKey]; [filter setValue:@(blur) forKey: @"inputRadius"];       //模糊图片      CIImage *result=[filter valueForKey:kCIOutputImageKey];       CGImageRef outImage=[context createCGImage:result fromRect:[result extent]];             UIImage *blurImage=[UIImage imageWithCGImage:outImage];                 CGImageRelease(outImage);       return blurImage;  }</code></pre>    <p>在Android上实现高斯模糊也可以使用原生的API-----RenderScript,不过需要Android的API是17以上,也就是Android 4.2版本。</p>    <pre>  <code class="language-java">/**       * 使用RenderScript实现高斯模糊的算法       * @param bitmap       * @return       */      public Bitmap blur(Bitmap bitmap){          //Let's create an empty bitmap with the same size of the bitmap we want to blur          Bitmap outBitmap = Bitmap.createBitmap(bitmap.getWidth(), bitmap.getHeight(), Bitmap.Config.ARGB_8888);          //Instantiate a new Renderscript          RenderScript rs = RenderScript.create(getApplicationContext());          //Create an Intrinsic Blur Script using the Renderscript          ScriptIntrinsicBlur blurScript = ScriptIntrinsicBlur.create(rs, Element.U8_4(rs));          //Create the Allocations (in/out) with the Renderscript and the in/out bitmaps          Allocation allIn = Allocation.createFromBitmap(rs, bitmap);          Allocation allOut = Allocation.createFromBitmap(rs, outBitmap);          //Set the radius of the blur: 0 < radius <= 25          blurScript.setRadius(20.0f);          //Perform the Renderscript          blurScript.setInput(allIn);          blurScript.forEach(allOut);          //Copy the final bitmap created by the out Allocation to the outBitmap          allOut.copyTo(outBitmap);          //recycle the original bitmap          bitmap.recycle();          //After finishing everything, we destroy the Renderscript.          rs.destroy();            return outBitmap;        }</code></pre>    <p>我们开发的图像框架 <a href="/misc/goto?guid=4959742232719862892" rel="nofollow,noindex">cv4j</a> 也提供了一个滤镜来实现高斯模糊。</p>    <pre>  <code class="language-java">GaussianBlurFilter filter = new GaussianBlurFilter();  filter.setSigma(10);    RxImageData.bitmap(bitmap).addFilter(filter).into(image2);</code></pre>    <p style="text-align:center"><img src="https://simg.open-open.com/show/6c947da2abd664c38c1bada69adc0022.png"></p>    <p style="text-align:center">使用RenderScript实现高斯模糊.png</p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/c2ef57fd0a022d627aea309c901b3683.png"></p>    <p style="text-align:center">使用cv4j实现高斯模糊.png</p>    <p>可以看出, cv4j 实现的高斯模糊跟RenderScript实现的效果一致。</p>    <p>其中,GaussianBlurFilter的代码如下:</p>    <pre>  <code class="language-java">public class GaussianBlurFilter implements CommonFilter {      private float[] kernel;      private double sigma = 2;      ExecutorService mExecutor;      CompletionService<Void> service;        public GaussianBlurFilter() {          kernel = new float[0];      }        public void setSigma(double a) {          this.sigma = a;      }        @Override      public ImageProcessor filter(final ImageProcessor src){          final int width = src.getWidth();          final int height = src.getHeight();          final int size = width*height;          int dims = src.getChannels();          makeGaussianKernel(sigma, 0.002, (int)Math.min(width, height));            mExecutor = TaskUtils.newFixedThreadPool("cv4j",dims);          service = new ExecutorCompletionService<>(mExecutor);            // save result          for(int i=0; i<dims; i++) {                final int temp = i;              service.submit(new Callable<Void>() {                  public Void call() throws Exception {                      byte[] inPixels = src.toByte(temp);                      byte[] temp = new byte[size];                      blur(inPixels, temp, width, height); // H Gaussian                      blur(temp, inPixels, height, width); // V Gaussain                      return null;                  }              });          }            for (int i = 0; i < dims; i++) {              try {                  service.take();              } catch (InterruptedException e) {                  e.printStackTrace();              }          }            mExecutor.shutdown();          return src;      }        /**       * <p> here is 1D Gaussian        , </p>       *       * @param inPixels       * @param outPixels       * @param width       * @param height       */      private void blur(byte[] inPixels, byte[] outPixels, int width, int height)      {          int subCol = 0;          int index = 0, index2 = 0;          float sum = 0;          int k = kernel.length-1;          for(int row=0; row<height; row++) {              int c = 0;              index = row;              for(int col=0; col<width; col++) {                  sum = 0;                  for(int m = -k; m< kernel.length; m++) {                      subCol = col + m;                      if(subCol < 0 || subCol >= width) {                          subCol = 0;                      }                      index2 = row * width + subCol;                      c = inPixels[index2] & 0xff;                      sum += c * kernel[Math.abs(m)];                  }                  outPixels[index] = (byte)Tools.clamp(sum);                  index += height;              }          }      }        public void makeGaussianKernel(final double sigma, final double accuracy, int maxRadius) {          int kRadius = (int)Math.ceil(sigma*Math.sqrt(-2*Math.log(accuracy)))+1;          if (maxRadius < 50) maxRadius = 50;         // too small maxRadius would result in inaccurate sum.          if (kRadius > maxRadius) kRadius = maxRadius;          kernel = new float[kRadius];          for (int i=0; i<kRadius; i++)               // Gaussian function              kernel[i] = (float)(Math.exp(-0.5*i*i/sigma/sigma));          double sum;                                 // sum over all kernel elements for normalization          if (kRadius < maxRadius) {              sum = kernel[0];              for (int i=1; i<kRadius; i++)                  sum += 2*kernel[i];          } else              sum = sigma * Math.sqrt(2*Math.PI);            for (int i=0; i<kRadius; i++) {              double v = (kernel[i]/sum);              kernel[i] = (float)v;          }          return;      }  }</code></pre>    <h2>空间卷积</h2>    <p>二维卷积在图像处理中会经常遇到,图像处理中用到的大多是二维卷积的离散形式。</p>    <p><img src="https://simg.open-open.com/show/dc5a10b71a0923d1250718c67426aee3.png"></p>    <p>二维卷积的离散形式.png</p>    <p>以下是 cv4j 实现的各种卷积效果。</p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/5e62ade34eb1898d45bfbb6a7b8a16f8.png"></p>    <p style="text-align:center">各种卷积效果1.png</p>    <p style="text-align:center"><img src="https://simg.open-open.com/show/1ef637c30069f584af71395888ee5282.png"></p>    <p style="text-align:center">各种卷积效果2.png</p>    <p>cv4j 目前支持如下的空间卷积滤镜</p>    <table>     <thead>      <tr>       <th>filter</th>       <th>名称</th>       <th>作用</th>      </tr>     </thead>     <tbody>      <tr>       <td>ConvolutionHVFilter</td>       <td>卷积</td>       <td>模糊或者降噪</td>      </tr>      <tr>       <td>MinMaxFilter</td>       <td>最大最小值滤波</td>       <td>去噪声</td>      </tr>      <tr>       <td>SAPNoiseFilter</td>       <td>椒盐噪声</td>       <td>增加噪声</td>      </tr>      <tr>       <td>SharpFilter</td>       <td>锐化</td>       <td>增强</td>      </tr>      <tr>       <td>MedimaFilter</td>       <td>中值滤波</td>       <td>去噪声</td>      </tr>      <tr>       <td>LaplasFilter</td>       <td>拉普拉斯</td>       <td>提取边缘</td>      </tr>      <tr>       <td>FindEdgeFilter</td>       <td>寻找边缘</td>       <td>梯度提取</td>      </tr>      <tr>       <td>SobelFilter</td>       <td>梯度</td>       <td>获取x、y方向的梯度提取</td>      </tr>      <tr>       <td>VarianceFilter</td>       <td>方差滤波</td>       <td>高通滤波</td>      </tr>      <tr>       <td>MaerOperatorFilter</td>       <td>马尔操作</td>       <td>高通滤波</td>      </tr>      <tr>       <td>USMFilter</td>       <td>USM</td>       <td>增强</td>      </tr>     </tbody>    </table>    <h2>总结</h2>    <p>cv4j 是 gloomyfish 和我一起开发的图像处理库,目前还处于早期的版本。</p>    <p>目前已经实现的功能:</p>    <p><img src="https://simg.open-open.com/show/ee7a0837d6dc2d5b6be39a4b46b543c4.png"></p>    <p style="text-align:center">cv4j.png</p>    <p>这周,我们对 cv4j 做了较大的调整,对整体架构进行了优化。还加上了空间卷积功能(图片增强、锐化、模糊等等)。接下来,我们会做二值图像的分析(腐蚀、膨胀、开闭操作、轮廓提取等等)</p>    <p> </p>    <p>来自:http://www.jianshu.com/p/41838f2d35c4</p>    <p> </p>