The high penetrating power， high resolution， and non-invasive nature of X-rays make them widely applicable in medical examinations， industrial flaw detection， and security screening. While X-ray Computed Tomography （CT） technology is extensively used and relatively mature， its significant radiation exposure， lengthy data acquisition time， and intricate mechanical structure hinder broader adoption. Furthermore， CT can not achieve three-dimensional imaging for certain flat samples. Computed Laminography （CL）， a derivative of CT technology， effectively addresses this limitation. However， prolonged exposure times and complex operational procedures still result in extended imaging durations and reduced efficiency， preventing in-situ observation. Consequently， there is an urgent need to develop a new technology capable of achieving high-efficiency， high-quality three-dimensional imaging of flat samples. Unlike visible light imaging， X-ray transmission methods yield images of relatively poor quality relative to the demands of stereoscopic imaging algorithms. Stereo imaging is a promising approach， as it requires only two images captured from different angles to achieve depth reconstruction. This method holds potential for enabling high spatio-temporal resolution stereoscopic imaging when combined with synchrotron radiation sources. Existing denoising algorithms prove ineffective for these images， failing to achieve satisfactory depth information recovery. Consequently， stereoscopic imaging techniques—well-established in the visible light domain—have not been successfully applied to X-ray imaging. This study focuses on the lack of dedicated denoising algorithms in the field of X-ray stereo vision imaging， and proposes a Gray Value Remapping Denoising Method （GVRM）. This method employs the NlMD function from the OpenCV open-source library during the preprocessing stage to perform initial noise reduction. It then enhances edge features using the Laplacian operator and further processes anomalous pixels with the NlMD function after adjusting its parameters. Subsequently， in the feature segmentation stage， a radial attenuation model is established based on Lambert-Beer law， and the Otsu's method is applied to achieve adaptive threshold segmentation， which supports multilevel gray-scale quantization. Finally， a normalized inter-correlation algorithm is used in the stereo matching stage to calculate the parallax field and generate high-precision parallax maps. In order to evaluate the advantages and disadvantages of the algorithms， the simulation of embedded wire in the pre-slab material and the nano-resolution low-latency imaging experiments at the synchrotron nanowire station were carried out， and the parallax calculations were performed on the images obtained by using the different processing methods， and the results were evaluated by using the standard parallax. Additionally， due to beam instability in synchrotron radiation experiments， which can cause spot jitter or brightness fluctuations， the jitter of the beam and various components may also shift the position of X-rays incident on the sample. This results in inconsistent projection positions at different angles， leading to image misalignment. Consequently， image registration is required. A simulated plate-like material embedded with filamentous objects was projected at two distinct angles， with added noise. The depth information of the filamentous objects was recovered， achieving a peak Signal-to-noise Ratio （SNR） of 17.28 dB and a Structural Similarity Index （SSI） of 0.92， outperforming depth information obtained from existing denoising algorithms. The experiments based on the BL18B at the Shanghai Synchrotron Radiation Facility show that the algorithm enables the parallax map to reach a peak signal-to-noise ratio of 40.54 dB， a root-mean-square error of 2.4， and a structural similarity index of 98.25%. The method breaks through the performance bottleneck of existing algorithms in complex noise scenarios， and achieves nano-resolution stereo vision imaging using synchrotron radiation， which significantly reduces the noise level of the parallax map of X-ray stereo vision imaging， improves the pixel-point parallax accuracy， and provides an effective solution for high spatial-resolution three-dimensional imaging of nano-scale samples. The success of this algorithm verifies the feasibility of the image noise reduction strategy based on the laws of physics in the field of X-ray stereo imaging， and provides a new paradigm for high-precision X-ray stereo vision imaging.