## 1 Introduction  
Image-based displacement measurement, typically with digital image correlation (DIC), is increasingly being used in the areas of structural dynamics and experimental mechanics by virtue of its attractive properties (e.g., non-contact and full-field measurement) [1; 2; 3; 4]. Three-dimensional (3D) DIC, which is a displacement-based approach, measures the 3D motion of surface patterns by analyzing stereo image pairs. This image-based technique can rapidly provide a high-density spatial measurement of a targeted vibrating surface. The use of high-speed cameras for dynamic measurements has attracted great attention in the field of modal testing of flexible structures, such as in the extraction of the modal model from DIC-based frequency response functions (FRFs) [5], the experimental modal analysis (EMA) of an artificial wing [6], EMA using wavelet transform [7], the measurement of mode shapes [8] and operating shapes [9], and the operational modal analysis of a flight [10], just to name a few. Full-field EMA, which extracts the modal model from DIC-based FRFs, has undergone in-depth study. Several practical aspects of performing an EMA test using 3D DIC have been presented, which include the test planning, setup, execution, and post-processing phases [11]. A few DIC-oriented modal identification methods have also been developed, e.g., Modan 3D [12], an enhanced FRF-based method [13]. Like the classical modal analysis with lumped sensors, the point-wise FRF is the fundamental element used for DIC-based EMA [5; 6; 11; 12; 13]. FRFs are calculated to be the Fourier transform of each output response measurement (e.g., displacements from subsets) divided by the Fourier transform of the input force, and the H\({}_{1}\) estimator is widely implemented for estimating DIC-based FRFs. The optically acquired FRF measurement has been tested in comparison with an accelerometer and laser Doppler vibrometer [14]; point-wise FRFs obtained by the 3D DIC method were shown to be less accurate at eight chosen specific points. A peculiar point-wise comparison was also addressed for optical technologies, and it was again seen to have a relevant drop in accuracy (by means of the coherence function) of DIC-based FRF measurements [15]. In fact, obtaining highly accurate FRFs from DIC measurements cannot be trivial due to hardware limitations, the intrinsic nature of the vision method, and so on. Optical measurement has been shown to favor high-displacement and low-frequency vibrations that are typically difficult to measure with accelerometers and laser vibrometers. Conversely, the primary disadvantage of the optical approach is that the measurable displacements diminish as a square of the frequency, and the displacements induced by modal excitation can fall below the noise floor of DIC at relatively high frequencies. The high noise floor can be mitigated in the following ways: (1) By using a shaker with a periodic input (e.g., pseudorandom), image averaging is applied while considering the fact that the corresponding time steps across each averaging frame should result in the same structural response, which, however, collapses the number of image frames that DIC is performed on by a factor equal to the number of averages. (2) Increasing the number of pixels across the areas of interest (AOI), which are defined at locations where measurements are desired, and averaging subset time histories within an AOI together can be carried out to reduce the noise level [11]. AOI averaging can cause bias error by ignoring the spatial variation of the FRF. (3) A lumped measurement (e.g., using an accelerometer) can be used to identify the eigenvalues, while the camera measurement is only used to produce the full-field mode shapes [16]. In addition to the optical sensor noise, the process noise is also present, which is mainly generated by the active cooling system of high-speed cameras. The process noise may not be effectively eliminated by the above-mentioned noise mitigation techniques.