Reducing / Enhancing known features in an image

I am microbiology student new to computer vision, so any help will be extremely appreciated.

This question involves microscope images that I am trying to analyze. The goal I am trying to accomplish is to count bacteria in an image but I need to pre-process the image first to enhance any bacteria that are not fluorescing very brightly. I have thought about using several different techniques like enhancing the contrast or sharpening the image but it isn't exactly what I need.

I want to reduce the noise(black spaces) to 0's on the RBG scale and enhance the green spaces. I originally was writing a for loop in OpenCV with threshold limits to change each pixel but I know that there is a better way.

Here is an example that I did in photo shop of the original image vs what I want.

Original Image and enhanced Image.

I need to learn to do this in a python environment so that I can automate this process. As I said I am new but I am familiar with python's OpenCV, mahotas, numpy etc. so I am not exactly attached to a particular package. I am also very new to these techniques so I am open to even if you just point me in the right direction.

Thanks!

I initially tried histogram equalization but did not get the desired results. So I used adaptive threshold using the mean filter:

th = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 3, 2)

Then I applied the median filter:

median = cv2.medianBlur(th, 5)

Finally I applied morphological closing with the ellipse kernel:

k1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
dilate = cv2.morphologyEx(median, cv2.MORPH_CLOSE, k1, 3)

THIS PAGE will help you modify this result however you want.