Based on the successful development of multi-parameter water quality detection system of UV-visible spectroscopy and the actual needs of accuracy, sensitivity, stability and other aspects in the measurement, the research is carried out to create a denoising algorithm of UV-visible spectroscopy on water quality detection based on Two-Dimension(2-D) restructuring and dynamic pane. As spectrometry water quality detection systems typically use low-cost industrial grade spectrometer, the CCD photon efficiency and stability are lower than research grade spectrometer, which is built with back-illuminated CCD and internal cooling thermostats. The output spectrum contains significant non-stationary noise, especially in the UV section and IR section. With the traditional denoising method, it is difficult to filter out the noise and retain spectral details at the same time. What’s more, in the case of online or in-situ real-time water quality measurement, the multiple-sample averaging method that commonly used in traditional denoising method may incur additional measurement error due to rapidly changed water sample. The new denoising algorithm proposed by this paper uses the continuous sampling with isochronous gap to expand spectral data into a 2-D matrix composed of spectrum and time axes. After a 2-D wavelet transformation, a variable-width pane which is able to slide horizontally in the coefficient matrix is set. The width of the pane is determined by the change rate of noise variance: the more rapid the rate changes, the narrower the width is. A dynamic denoising threshold is calculated with the wavelet coefficients in the pane and a threshold vector is created with pane sliding. Finally, the spectrum can be denoised by the threshold vector with wavelet shrinkage method. The experimental results show that this denoising algorithm not only removes the spectral non-stationary noise effectively, but also retains the spectral details, which is helpful to improve the accuracy of the instrument. Meanwhile, since time-domain average is not used here, the impact to the denoising performance on fast-changing of water samples is small, which is suitable for the online or in-situ water quality detection environment.