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.
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