Traditional denoising methods for frequency-hopping (FH) signals parameter estimation often fail to effectively preserve the boundaries of FH signals in the time-frequency graph, resulting in low accuracy in estimating the time parameters of FH signals. To address this, a denoising method for FH signals time-frequency graphs based on local energy thresholding is proposed. Firstly, to increase the energy proportion of FH signals in the time-frequency graph after short-time Fourier transform, instantaneous frequency operators are used to mark and remove time-frequency coefficients that do not match the frequency of the FH signals as noise. Then, to avoid losing the energy of the FH signals during denoising, a search window is set to locate the area with the highest energy density in the time-frequency graph, and thresholds are adaptively set for denoising based on the energy distribution in different areas. Finally, a synchronous compression method is used to compress the time-frequency coefficients to the position of the local energy centroid, making the boundaries of the FH signals in the time-frequency graph clearer. Experimental results show that this method can simultaneously improve the accuracy of time and frequency parameter estimation of FH signals when the signal-to-noise ratio is greater than -5 dB, with normalized mean square errors below 0.1 and 0.2, respectively.