||In the last two decades, the advance in nighttime light (NTL) remote sensing has fueled a surge in extensive research towards mapping human footprints. Nevertheless, the full potential of NTL data is largely constrained by the blooming effect. In this study, we propose a new concept, the Pixel Blooming Effect (PiBE), to delineate the mutual influence of lights from a pixel and its neighbors, and an integrated framework to eliminate the PiBE in radiance calibrated DMSP-OLS datasets (DMSPgrc). First, lights from isolated gas flaring sources and a Gaussian model were used to model how the PiBE functions on each pixel through point spread function (PSF). Second, a two-stage deblurring approach (TSDA) was developed to deconvolve DMSPgrc images with Tikhonov regularization to correct the PiBE and reconstruct PiBE-free images. Third, the proposed framework was assessed by synthetic data and VIIRS imagery and by testing the resulting image with two applications. We found that high impervious surface fraction pixels (ISF > 0.6) were impacted by the highest absolute magnitude of PiBE, whereas NTL pattern of low ISF pixels (ISF < 0.2) was more sensitive to the PiBE. By using TSDA the PiBE in DMSPgrc images was effectively corrected which enhanced data variation and suppressed pseudo lights from non-built-up pixels in urban areas. The reconstructed image had the highest similarity to reference data from synthetic image (SSIM = 0.759) and VIIRS image (r = 0.79). TSDA showed an acceptable performance for linear objects (width > 1.5 km) and circular objects (radius > 0.5 km), and for NTL data with different noise levels (<0.6σ). In summary, the proposed framework offers a new opportunity to improve the quality of DMSP-OLS images and subsequently will be conducive to NTL-based applications, such as mapping urban extent, estimating socioeconomic variables, and exploring eco-impact of artificial lights.