It is possible to take the background image and use ImageJ or Imagemagick tools to substract that background to the rest of the images in batch. Both sofware allow you to write simply macros or scripts to do so.

In the "hardware way", depending on the type of microscope (regular, inverted,etc) you can hack the ligth stream by using a semitransparent material to generate a difuse light. Maybe with some parafilm, and perhaps you have to increse the intensity of the light.

It is possible to take the background image and use ImageJ or Imagemagick tools to substract that background to the rest of the images in batch. Both sofware allow you to write simply macros or scripts to do so.

In the "hardware way", depending on the type of microscope (regular, inverted,etc) you can hack the ligth stream by using a semitransparent material to generate a difuse light. Maybe with some parafilm, and perhaps you have to increse the intensity of the light.

If the image was captured in multiphoton LSM, shading correction application can also help. In fluorescent Imaging system, playing around the aperture while increasing or decreasing the light intensity can yield uniform illumination so far as the Koehler illumination is set correctly

Sometimes I am getting my CCD camera on TEM misaligned and then my pictures have uneven background. In most cases (unfortunately not always) ImageJ can help.

Open picture, go to Process-FFT-Bandpass filter, set “Small structures” to 0, “Large structures” to about ¾ the size of field of view, click OK. For my 10 Megapixels pictures I am setting “large structures” value to 2000-3500 (needs some tweaking).

For shading correction in ImageJ you need the Shading Correction plugin which you can download here:

http://rsb.info.nih.gov/ij/plugins/shading-corrector.html

As David mentioned you can use this to correct using an image captured with the same microscope and camera settings, but without the sample. You rename that background image to "blank-field" and run the plugin.

The standard ImageJ plugin has the nasty habit of not checking for overflow of the data type after correction (for instance a value of 300 for an 8-bit image) and this can really give some weird results. I've modified the plugin to prevent this happening, and it also does correftion of colour images slightly differently, by correcting only the brightness, not the hue or saturation, of images. If you want I can email you my version.

I agree that having a good image of an "empty" uniform field with the same optical path and exposure conditions and doing a "flat-field correction" is ideal and can give fantastic results. The pitfall is that this correction image can also introduce any of its peculiarities into your final image. For this reason I like to average 5-10 of these empty field or uniform samples to to avoid any debris or imperfections from this uniform image from getting prominently stamped into your final corrected image. You need to convert the images into a floating point format before doing this. Corrected image = [(experimental image- background) ÷ (uniform image- background)]*average pixel value of experimental image. The corrected image can then be converted back to whatever data type is needed. This works well for bright field and wide-field epi-fluorescence, but for fluorescence you need a uniform fluorescent sample, which can be a challenge. We use supported lipid bilayers with fluorescent lipids, which are bright, uniform samples. With confocal and TIRFM, subtle change in focus change the illumination so can be tricky go get this just right. The strength of this approach is that its completely quantitative, but more work than the other methods as you probably need new uniform images any time there is a change in the optical path, even subtle ones.

If you don't have the pre-captured flatfield images, but have a bunch of images taken with the same configuration but different fields, you may be able to opportunistically use an empty sub-region of one experimental image as a "uniform" image for another. You just need to crop down to the cell of interest and find exactly the same area (transfer of ROI) of another image where there is no cell or debris (use that are the uniform image). I have gotten this to work with DIC and interference reflection images of lymphocytes where they are a small fraction of the field. Can get publication quality images of single cells and contact areas if you need to do this retrospectively and have lots of images of different fields to choose from.

You can minimize it by applying protocol to correct uneven illumination. Take the raw images of your samples, then take images of the illuminated field of view without the sample (flat field image). You can also take a picture of the field of view without any light (dark field, to correct for the noise of the camera). Then, open all digital images (tiff format) in ImageJ. Use image calculation function to do: (Raw-Dark field)/(flat - dark field), click to show the result in 32 bit.

I find that when the imageJ shading correction plugin is used with RGB tiff files, it is best to separate the colors in both the blank-field image and the image to be corrected,and then to correct each color independently and to merge the corrected images back into RGB. If there are many images to correct with the same blank-field image,put all of the images into a stack, then separate the stack into colors, run the shading correction on each of the three stacks, merge the colors, and then if needed, run "stack to images." For a reality check and convenience, include a copy of the blank-field in the original RGB stack. After separating the colors and running the shading corrector, the corrected image of the copy of the blank-field should be completely uniform.

Be careful not to have any saturated pixels in the blank-field image or the image of interest. Saturated pixels in the image of interest will be too dark after correction, while saturated pixels in the blank-field will cause the corresponding pixels in the image of interest to be too bright.

I am surprised how often the substraction of the blank-field Image is thought to be the right way to perform the correction. The best way to explain why this is not the right way to do it is an experiment in thought . If one reduces the intensity of the illuminating source then the pixel values of the image will be reduced in a PROPORTIONAL manner and not by substraction. Thus, in reality the blank field intensity is multiplied by the modulation produced by the image of interest. Therefore, the right way to perform the correction is by dividing the image of interest by the blank field Image and to multiply the result by 255. The variable for the division should not be an integer but floating point. The conversion to 8 bit can be done after the multiplication.

What happens? In areas of the image where no modulation occurs by the image of interest the result of the division is one, or say very close to one due to noise on both of the images. The multiplication by 255 gives a value that corresponds to white and this is valid NO matter how large the pixel value was in the original images. In places where the image of interest does modulate the blank field the effect is a local contrast enhancement. In cases where the result of the modulation by the image of interest is higher than the intensity of the blank Image, e.g. interference microscopes, focusing objects in the field or strong gradients it may be necessary to reduce the factor of 255 to avoid Overflow.

However, there a a few pitfalls to this procedure. The first is that the intensity of the illuminating source should be exactly!! the same for both Images. This is not necessarily true for older microscopes that were illuminated by halogen bulbs. The bulbs were often driven by a simple transformer, thus the intensity of the Image is modulated with the double frequency of the mains. This can not be seen with the eye, but if the Integration time of the camera is comparable or less to half of the period of the mains the Images can fluctuate in intensity. In good microscopes the fluctuation is normally smaller than can be perceived by the eye because we detect light intensity logarithmically. One way to avoid this pitfall is to capture not only one Image for each case but a few and to average them and then perform the Division this also reduces noise.

The second pitfall is that if the flat field Image contains Pixel with the value 0 or Close to 0. This can be the case when the field aperture of the microscope is not sufficiently open to illuminate all areas of the Image. The division would result in an overflow and even if the pixel value is not 0 but 1 or 2 the division gives large jumps of intensity in the resulting image and thus enhanses noise. Therefore, it is better to exclude areas where the blank Image has a value lower than a small value, say e.g. 10 or 30 and to automatically set the result to 0.