Here are representative flatfields in all three cameras. The images shown here are inverted, meaning that dark areas have higher relative sensitivity while bright areas have lower relative sensitivity. This is the way flatfield reference files (used in the calibration pipeline) appear, because the pipeline *multiplies* the data by the reference file.
In un-flattened images with background signal, the background will be modulated by this sensitivity pattern, so un-flattened data will look a bit like the inverse of the images shown here. The large scale structure seen in these images is somewhat wavelength dependent, and so will be slightly different depending upon which filter the flat was taken through. The "pedestal effect," or any incorrect dark (bias) subtraction, can also imprint the flat field pattern on data. Please see the section on pedestal below.
There are as many different cures for flatfield problems as there are observers. If your data still retain flatfield features after correcting for pedestal, bias jumps, and the like, you may want to reprocess the data with an enhanced flatfield. Add or subtract a constant (depending on whether the pipeline flat over- or under-corrects the data) to a copy of the pipeline flat, renormalize it, and reprocess the data using this flat. Alternatively, you may have objects with unusual colors in your image. If so, you may want to make a color-dependent flat field and process your data with that.
Temperature-Specific NICMOS Flatfield Generator is available to facilitate the making of flatfield reference files corresponding to a specific temperature.