Conventional local-based color reconstruction for a white-RGB (WRGB) imaging system relies excessively on reference pixels. Therefore, it is sensitive to noisy interference. To address this issue, we propose an improved nonlocal fuzzy color segmentation-based color reconstruction hybrid approach for a WRGB imaging system. Unlike local-based approaches, we attempt to reproduce color information based on the statistical color distribution of the raw sensor data. According to the distribution analysis, the color distribution (histogram and cumulative distribution function) is close to that of the full-resolution image. However, brief histogram matching gives rise to zipper artifacts, which result from the multicombination of the red, green, and blue corresponding to one white. Therefore, a hybrid color segmentation is proposed to address this issue. The first step is a brief sorting-based color segmentation in the hue channel. Fuzzy-based color segmentation is then utilized to acquire more subregions in the proposed saturation space. Finally, fast histogram matching is carried out to obtain the full-color information for the white pixel for each region. Compared with state-of-the-art approaches, the proposed nonlocal hybrid approach is capable of significantly reducing the influence of noise with higher peak signal-to-noise ratios. Furthermore, according to the hybrid color segmentation, zipper artifacts are successfully avoided.