Detection of Blur in Images/Video sequences

I had asked this on photo stackexchange but thought it might be relevant here as well, since I want to implement this programatically in my implementation.

I am trying to implement a blur detection algorithm for my imaging pipeline. The blur that I want to detect is both -

1) Camera Shake: Pictures captured using hand which moves/shakes when shutter speed is less.

2) Lens focussing errors - (Depth of Field) issues, like focussing on a incorrect object causi开发者_如何学Gong some blur.

3) Motion blur: Fast moving objects in the scene, captured using a not high enough shutter speed. E.g. A moving car a night might show a trail of its headlight/tail light in the image as a blur.

How can one detect this blur and quantify it in some way to make some decision based on that computed 'blur metric'?

What is the theory behind blur detection?

I am looking of good reading material using which I can implement some algorithm for this in C/Matlab.

thank you.

-AD.

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Motion blur and camera shake are kind of the same thing when you think about the cause: relative motion of the camera and the object. You mention slow shutter speed -- it is a culprit in both cases.

Focus misses are subjective as they depend on the intent on the photographer. Without knowing what the photographer wanted to focus on, it's impossible to achieve this. And even if you do know what you wanted to focus on, it still wouldn't be trivial.

With that dose of realism aside, let me reassure you that blur detection is actually a very active research field, and there are already a few metrics that you can try out on your images. Here are some that I've used recently:

• Edge width. Basically, perform edge detection on your image (using Canny or otherwise) and then measure the width of the edges. Blurry images will have wider edges that are more spread out. Sharper images will have thinner edges. Google for "A no-reference perceptual blur metric" by Marziliano -- it's a famous paper that describes this approach well enough for a full implementation. If you're dealing with motion blur, then the edges will be blurred (wide) in the direction of the motion.
• Presence of fine detail. Have a look at my answer to this question (the edited part).
• Frequency domain approaches. Taking the histogram of the DCT coefficients of the image (assuming you're working with JPEG) would give you an idea of how much fine detail the image has. This is how you grab the DCT coefficients from a JPEG file directly. If the count for the non-DC terms is low, it is likely that the image is blurry. This is the simplest way -- there are more sophisticated approaches in the frequency domain.

There are more, but I feel that that should be enough to get you started. If you require further info on either of those points, fire up Google Scholar and look around. In particular, check out the references of Marziliano's paper to get an idea about what has been tried in the past.

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There is a great paper called : "analysis of focus measure operators for shape-from-focus" (https://www.researchgate.net/publication/234073157_Analysis_of_focus_measure_operators_in_shape-from-focus) , which does a comparison about 30 different techniques.

Out of all the different techniques, the "Laplacian" based methods seem to have the best performance. Most image processing programs like : MATLAB or OPENCV have already implemented this method . Below is an example using OpenCV : http://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/

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One important point to note here is that an image can have some blurry areas and some sharp areas. For example, if an image contains portrait photography, the image in the foreground is sharp whereas the background is blurry. In sports photography, the object in focus is sharp and the background usually has motion blur. One way to detect such a spatially varying blur in an image is to run a frequency domain analysis at every location in the image. One of the papers which addresses this topic is "Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes" (cvpr2017).

the authors look at multi resolution DCT coefficients at every pixel. These DCT coefficients are divided into low, medium, and high frequency bands, out of which only the high frequency coefficients are selected. The DCT coefficients are then fused together and sorted to form the multiscale-fused and sorted high-frequency transform coefficients A subset of these coefficients are selected. the number of selected coefficients is a tunable parameter which is application specific. The selected subset of coefficients are then sent through a max pooling block to retain the highest activation within all the scales. This gives the blur map as the output, which is then sent through a post processing step to refine the map. This blur map can be used to quantify the sharpness in various regions of the image. In order to get a single global metric to quantify the bluriness of the entire image, the mean of this blur map or the histogram of this blur map can be used

Here are some examples results on how the algorithm performs:

The sharp regions in the image have a high intensity in the blur_map, whereas blurry regions have a low intensity.

The github link to the project is: https://github.com/Utkarsh-Deshmukh/Spatially-Varying-Blur-Detection-python

The python implementation of this algorithm can be found on pypi which can easily be installed as shown below:

pip install blur_detector

A sample code snippet to generate the blur map is as follows:

import blur_detector
import cv2
if __name__ == '__main__':
    img = cv2.imread('image_name', 0)
    blur_map = blur_detector.detectBlur(img, downsampling_factor=4, num_scales=4, scale_start=2, num_iterations_RF_filter=3)

    cv2.imshow('ori_img', img)
    cv2.imshow('blur_map', blur_map)
    cv2.waitKey(0)

• For detecting blurry images, you can tweak the approach and add "Region of Interest estimation". In this github link: https://github.com/Utkarsh-Deshmukh/Blurry-Image-Detector , I have used local entropy filters to estimate a region of interest. In this ROI, I then use DCT coefficients as feature extractors and train a simple multi-layer perceptron. On testing this approach on 20000 images in the "BSD-B" dataset (http://cg.postech.ac.kr/research/realblur/) I got an average accuracy of 94%

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Just to add on the focussing errors, these may be detected by comparing the psf of the captured blurry images (wider) with reference ones (sharper). Deconvolution techniques may help correcting them but leaving artificial errors (shadows, rippling, ...). A light field camera can help refocusing to any depth planes since it captures the angular information besides the traditional spatial ones of the scene.