Fully diffuse seismic wavefields are ideal for Ambient Noise Imaging (ANI) of subsurface structures. However, the lack of feasible methods to identify highly diffuse wave hampers applications of ANI for imaging including evaluation of seismic attenuation and temporal changes with high temporal resolution. Conventional ANI approaches require data normalization, which results in significant loss of amplitude and phase information. Here we propose a method to quantitatively evaluate the degree of diffuseness of seismic wavefields by analyzing their statistical characteristics of modal amplitudes in the frequency domain. Tests on synthetic waveform and real data show that the method can effectively distinguish between diffuse and non-diffuse waveforms. By identifying a 60-second-long diffuse coda of a local M 2.2 earthquake recorded by a dense nodal array on the San Jacinto Fault Zone, we successfully extract high-quality dispersion curve and Q-value without performing data normalization. Our proposed method can advance the imaging of subsurface velocity and attenuation structures and monitoring temporal changes for scientific studies and engineering applications.