+(UIImage *)coreBlurImage:(UIImage *)image withBlurNumber:(CGFloat)blur 
{ 
  CIContext *context = [CIContext contextWithOptions:nil]; 
  CIImage *inputImage= [CIImage imageWithCGImage:image.CGImage]; 
  //設置filter
  CIFilter *filter = [CIFilter filterWithName:@"CIGaussianBlur"]; 
  [filter setValue:inputImage forKey:kCIInputImageKey]; [filter setValue:@(blur) forKey: @"inputRadius"]; 
  //模糊圖片
  CIImage *result=[filter valueForKey:kCIOutputImageKey]; 
  CGImageRef outImage=[context createCGImage:result fromRect:[result extent]];    
  UIImage *blurImage=[UIImage imageWithCGImage:outImage];      
  CGImageRelease(outImage); 
  return blurImage;
}

/**
   * 使用RenderScript實現(xiàn)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;
}

GaussianBlurFilter filter = new GaussianBlurFilter();
filter.setSigma(10);

RxImageData.bitmap(bitmap).addFilter(filter).into(image2);

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;
	}
}