Abstract: In long-term time-lapse fluorescence imaging with genetically encoded fluorescence sensors, cell proliferation gradually fills the field of view, leaving few cell-free regions for background annotation. Both manual region-of-interest (ROI) selection and automated thresholding methods fail under these conditions, leading to systematic overestimation of background intensity in late frames. This study proposes an exponential decay modeling approach for background prediction that transfers parameters estimated under low-density conditions to high-density wells. Background fluorescence intensity from five sensors (SoNar, iNap1, iNapc, HyPerion, and HyPerion-C) was recorded at two excitation channels (405 nm and 465 nm) over 55 frames at 30-minute intervals under both 20% and 40% initial density, yielding over 6000 annotated frames. An exponential relaxation model was fitted to the 20% density data using joint nonlinear least squares, sharing the decay time constant and asymptotic background intensity across wells while allowing each well to retain an independent initial intensity. These shared parameters were then transferred to 40% density wells, where only the first 5–10 frames were used to estimate the well-specific initial intensity via a closed-form weighted least-squares solution. The transfer method achieved a median mean absolute error (MAE) of 150–350 analog-to-digital units (ADU) for SoNar, iNap1, and iNapc, substantially outperforming both the self-fit and copy-last baselines when few initialisation frames were available. This approach is applicable to any fluorescence time-lapse imaging scenario where background undergoes monotonic decay and late-frame annotation becomes impractical.