How can I sharpen an image in OpenCV

Question

How can I sharpen an image using OpenCV  There are many ways of smoothing or blurring but none that I could see of sharpening

User · Answer

Any Image is a collection of signals of various frequencies. The higher frequencies control the edges and the lower frequencies control the image content. Edges are formed when there is a sharp transition from one pixel value to the other pixel value like 0 and 255 in adjacent cell. Obviously there is a sharp change and hence the edge and high frequency. For sharpening an image these transitions can be enhanced further.

One way is to convolve a self made filter kernel with the image.

    import cv2
    import numpy as np

    image = cv2.imread('images/input.jpg')
    kernel = np.array([[-1,-1,-1], 
                       [-1, 9,-1],
                       [-1,-1,-1]])
    sharpened = cv2.filter2D(image, -1, kernel) # applying the sharpening kernel to the input image & displaying it.
    cv2.imshow('Image Sharpening', sharpened)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

There is another method of subtracting a blurred version of image from bright version of it. This helps sharpening the image. But should be done with caution as we are just increasing the pixel values. Imagine a grayscale pixel value 190, which if multiplied by a weight of 2 makes if 380, but is trimmed of at 255 due to the maximum allowable pixel range. This is information loss and leads to washed out image.

    addWeighted(frame, 1.5, image, -0.5, 0, image);

User · Answer

You can sharpen an image using an unsharp mask  You can find more information about unsharp masking here  And here s a Python implementation using OpenCV   import cv2 as cv import numpy as np  def unsharp mask image  kernel size  5  5   sigma 1 0  amount 1 0  threshold 0          Return a sharpened version of the image  using an unsharp mask         blurred   cv GaussianBlur image  kernel size  sigma      sharpened   float amount   1    image - float amount    blurred     sharpened   np maximum sharpened  np zeros sharpened shape       sharpened   np minimum sharpened  255   np ones sharpened shape       sharpened   sharpened round   astype np uint8      if threshold  gt  0          low contrast mask   np absolute image - blurred   lt  threshold         np copyto sharpened  image  where low contrast mask      return sharpened  def example        image   cv imread  my-image jpg       sharpened image   unsharp mask image      cv imwrite  my-sharpened-image jpg   sharpened image

User · Answer

For clarity in this topic  a few points really should be made    Sharpening images is an ill-posed problem  In other words  blurring is a lossy operation  and going back from it is in general not possible   To sharpen single images  you need to somehow add constraints  assumptions  on what kind of image it is you want  and how it has become blurred  This is the area of natural image statistics  Approaches to do sharpening hold these statistics explicitly or implicitly in their algorithms  deep learning being the most implicitly coded ones   The common approach of up-weighting some of the levels of a DOG or Laplacian pyramid decomposition  which is the generalization of Brian Burns answer  assumes that a Gaussian blurring corrupted the image  and how the weighting is done is connected to assumptions on what was in the image to begin with   Other sources of information can render the problem sharpening well-posed  Common such sources of information is video of a moving object  or multi-view setting  Sharpening in that setting is usually called super-resolution  which is a very bad name for it  but it has stuck in academic circles   There has been super-resolution methods in OpenCV since a long time     although they usually dont work that well for real problems last I checked them out  I expect deep learning has produced some wonderful results here as well  Maybe someone will post in remarks on whats worthwhile out there

User · Answer

You can try a simple kernel and the filter2D function  e g  in Python   kernel   np array   -1 -1 -1    -1 9 -1    -1 -1 -1    im   cv2 filter2D im  -1  kernel    Wikipedia has a good overview of kernels with some more examples here - https   en wikipedia org wiki Kernel  image processing      In image processing  a kernel  convolution matrix  or mask is a small matrix  It is used for blurring  sharpening  embossing  edge detection  and more  This is accomplished by doing a convolution between a kernel and an image

User · Answer

You can find a sample code about sharpening image using  unsharp mask  algorithm at OpenCV Documentation   Changing values of sigma threshold amount will give different results      sharpen image using  unsharp mask  algorithm Mat blurred  double sigma   1  threshold   5  amount   1  GaussianBlur img  blurred  Size    sigma  sigma   Mat lowContrastMask   abs img - blurred   lt  threshold  Mat sharpened   img  1 amount    blurred  -amount   img copyTo sharpened  lowContrastMask

User · Answer

You can also try this filter sharpen filter   np array   0  -1  0    -1  5  -1    0  -1  0    sharped img   cv2 filter2D image  -1  sharpen filter

User · Answer

To sharpen an image we can use the filter  as in many previous answers  kernel   np array   -1  -1  -1   -1  8  -1   -1  -1  0    np float32    kernel    denominator   kernel  It will be the most when the denominator is 1 and will decrease as increased  2 3    The most used one is when the denominator is 3  Below is the implementation  kernel   np array   -1  -1  -1   -1  8  -1   -1  -1  0    np float32    kernel   1 3   kernel  dst   cv2 filter2D image  -1  kernel

User · Answer

Try with this  cv  bilateralFilter img  9  75  75    You might find more information here

User · Answer

One general procedure is laid out in the Wikipedia article on unsharp masking  You use a Gaussian smoothing filter and subtract the smoothed version from the original image  in a weighted way so the values of a constant area remain constant   To get a sharpened version of frame into image   both cv  Mat  cv  GaussianBlur frame  image  cv  Size 0  0   3   cv  addWeighted frame  1 5  image  -0 5  0  image    The parameters there are something you need to adjust for yourself  There s also Laplacian sharpening  you should find something on that when you google

[image-processing] How can I sharpen an image in OpenCV?

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