Adaptive threshold with blurry image

Question

I'm in search for best Adaptive Threshold method for image binarization. But I have any problems with dark and blurry image.
Input image:

and when I use Adaptive threshold method I receive this
Output Image:

This is not good for me!
So, could someone help me fix this problem?

---

another image :

and :

the first seem very bad with @Hammer'solution (i must chose c channel) , the second i can use adaptive threshold normal .
so i want to find the best solution for all cases .

thank Again !

Answer 1

It seems like color is a much better indicator for segmentation in your image than intensity. Try converting it to HSV and then running OTSU on the H channel.

in python

hsv = cv2.cvtColor(image, cv2.cv.CV_BGR2HSV)
cv2.imshow('hsv', hsv[:,:,0])
(thresh, im_bw) = cv2.threshold(hsv[:,:,0], 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
cv2.imshow('OTSU', im_bw)

gives (hsv)

and then (OTSU)

A little eroding and dilating and you should be good to go

Answer 2

If a single threshold is not enough to separate all images, you could try the Watershed algorithm with two thresholds.

Use a high threshold to get an image with segments that are definitely part of a digit, and a high inverse threshold to get an image with segments that are definitely not part of a digit.

Erode both images a bit to increase the certainty.

Then use the 2 images as seeds for Watershed.

Here is an answer where this is done

Answer 3

You might be interested in these adaptive thresholds used by openCV.

I used the adaptive mean threshold. You may have to play with the parameters a bit, but if your images are similar (same size etc), hopefully there won't be too much tweaking required.

# Smooth image
filtered = cv2.adaptiveThreshold(input_image.astype(np.uint8), 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 41, 3)

# Some morphology to clean up image
kernel = np.ones((5,5), np.uint8)
opening = cv2.morphologyEx(filtered, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)

Results:

Answer 4

The following code ...

im=cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,111,3)
cv2.imshow('mkm',im)`

... gives a good result: