The increasing demand for intelligent, wearable health monitoring devices with a photoplethysmogram (PPG) sensor to monitor real-time heart rate (HR) in a subject attracts great attention from researchers. However, the PPG-enabled wearable devices are sensitive to motion artifacts, which results in inaccurate HR estimation. The accuracy of HR estimation is possible only by eliminating motion artifacts. A method based on multiresolution spectro-temporal super-resolution (STSR) and superlet transform (SLT) is proposed to improve the estimation accuracy of a motion artifact corrupted PPG signal. This proposed methodology does not require a reference accelerometer signal to suppress motion artifacts to estimate HR in real time during physical activities. Hence, the proposed method is a computationally efficient algorithm. In the first step, a multiorder SLT separates the motion artifacts from the acquired PPG signal in the proposed method. In the next step, a high-intensity lobe in the spectro-temporal representation of the SLT spectrogram is detected to compute the HR. In the final step, an HR smoothing algorithm that uses HR from the preceding windows is proposed to accurately estimate HR in a motion artifact effected signal. The performance of the SLT-based HR estimation algorithm is evaluated using the publicly available IEEE Signal Processing Cup (SPC) dataset. The average HR estimation error is 1.01 beats per minute, and the Pearson correlation is 0.997. The low estimation error, short computational time, and fast HR tracking make the proposed method an ideal choice to implement in wearable devices for real-time HR estimation.