A High Dynamic Range (HDR) image captures a wider range of brightness levels than a Standard Dynamic Range (SDR) image. Many traditional image-processing techniques, such as edge detection, contrast adjustment, and sharpening, cannot be applied directly to HDR images due to their wide luminance range. Therefore, one way of analyzing HDR images is by applying exposure bracketing, which is a technique to take multiple shots of the same scene at different exposures. The images with different exposures are processed separately, and the results of this processing are combined to draw a conclusion about the HDR image. This approach allows for a detailed analysis of the image’s dynamic range, details, and overall visual quality by examining how each exposure contributes to the final HDR representation. This paper introduces Virtual Exposure Bracketing (VEB), a novel approach that synthesizes bracketed exposures from a single HDR image using fuzzy memberships. By modeling luminance distributions with fuzzy memberships, our method estimates virtual exposure values (EVs) that preserve details in both highlight (bright) and shadow (dark) regions. Experimental results demonstrate that VEB effectively generates multi-exposure images from an HDR source. The proposed approach offers a computationally efficient, artifact-free solution for HDR imaging and is particularly advantageous for synthetic HDR images, where existing exposure bracketing methods may not work well.