Modern applications of biological microscopy such as high-content screening (HCS), 4D imaging, and multispectral imaging may involve collection of thousands of images in every experiment making efficient image-compression techniques necessary. Reversible compression algorithms, when used with biological micrographs, provide only a moderate compression ratio, while irreversible techniques obtain better ratios at the cost of removing some information from images and introducing artifacts. We construct a model of noise, which is a function of signal in the imaging system. In the next step insignificant intensity levels are discarded using intensity binning. The resultant images, characterized by sparse intensity histograms, are coded reversibly. We evaluate compression efficiency of combined reversible coding and intensity depth-reduction using single-channel 12-bit light micrographs of several subcellular structures. We apply local and global measures of intensity distribution to estimate maximum distortions introduced by the proposed algorithm. We demonstrate that the algorithm provides efficient compression and does not introduce significant changes to biological micrographs. The algorithm preserves information content of these images and therefore offers better fidelity than standard irreversible compression method JPEG2000.