  Recovering the wavelength from disordered speckle patterns has become an
exciting prospect as a wavelength measurement method due to its high resolution
and simple design. In previous studies, panel cameras have been used to detect
the subtle differences between speckle patterns. However, the volume,
bandwidth, sensitivity, and cost (in non-visible bands) associated with panel
cameras have hindered their utility in broader applications, especially in high
speed and low-cost measurements. In this work, we broke the limitations imposed
by panel cameras by using a quadrant detector (QD) to capture the speckle
images. In the scheme of QD detection, speckle images are directly filtered by
convolution, where the kernel is equal to one quarter of a speckle pattern.
First, we proposed an up-sampling algorithm to pre-process the QD data. Then a
new convolution neural network (CNN) based algorithm, shallow residual network
(SRN), was proposed to train the up-sampled images. The experimental results
show that a resolution of 4 fm (~ 0.5 MHz) was achieved at 1550nm with an
updating speed of ~ 1 kHz. More importantly, the SRN shows excellent
robustness. The wavelength can be precisely reconstructed from raw QD data
without any averaging, even where there exists apparent noise. The low-cost,
simple structure, high speed and robustness of this design promote the
speckle-based wavemeter to the industrial grade. In addition, without the
restriction of panel cameras, it is believed that this wavemeter opens new
routes in many other fields, such as distributed optical fiber sensors, optical
communications, and laser frequency stabilization.
