Computed Laminography (CL) technology is well-suited for non-destructive testing of flat objects. Orthogonal Translation CL (OTCL) imaging has the advantages of simple system structure and high resolution in two orthogonal directions, but it also suffers from of incomplete projection data and serious aliasing artifacts in traditional image reconstruction algorithms. Image reconstruction based on prior information can effectively solve the ill-conditioned reconstruction problem caused by missing projection data. This means that, integrating prior information into the incomplete data image reconstruction process can enhance the quality of the reconstructed image. Nevertheless, aligning the prior information with the reconstructed object is crucial, as there is often a deviation between the prior information and the actual detected object. Obtaining accurate prior information requires substantial preprocessing time.To address the issue of aliasing artifacts in OTCL image reconstruction, this paper proposes a reconstruction algorithm based on Self-Prior Information-TV constraint (SPI-TV). The algorithm includes two processes: self-prior information acquisition based on SAA reconstructed images and OTCL image reconstruction based on self-prior-TV constraints. The image reconstruction process includes an iterative step based on its own prior and a TV denoising step based on the Split Bregman (SB) framework. The experimental comparison methods used were SAA, SART and SART-TV. The quality of the reconstructed image was comprehensively evaluated using three sets of quantitative indicators: Root Mean Square Error (RMSE), Universal Quality Index (UQI), and Structural Similarity Index (SSIM). The Peak Signal to Noise Ratio (PSNR) is introduced to evaluate noisy experiments.This paper conducts simulation experiments with noiseless and noisy on the phantom. The results prove that SPI-TV can effectively eliminate aliasing artifacts while suppressing the blur of image edge information and noise. The result is the best performance at the edge of the structure, without toothed contours, and the image contrast and the quality of image reconstruction are high. At the same time, we analyzed the grayscale distribution map. Comparing the grayscale distribution curve of the true image, the reconstruction results of the SART and SART-TV algorithms have obvious jumps in gray values, while the SPI-TV algorithm can better approximate the grayscale curve of the true image, further verifying that performance of the SPI-TV algorithm. For Parallel Translation Computed Laminography (PTCL) scanning, the method used to obtain a priori mask cannot completely obtain the edge information of the reconstructed structure because it only scans in one direction. The prior information is obtained by performing PTCL scanning and OTCL scanning on the phantom (taking the 16th layer as an example). It can be seen that only edge information in a single direction can be obtained from a single segment of scanned data, while the other direction includes interference from adjacent layers. Therefore, the proposed method is suitable for OTCL scanning, and obtaining accurate structural prior information requires vertical scanning data from two sections.Compared with previous image reconstruction methods based on prior information, the proposed method does not require additional information to obtain the prior structure, which greatly reduces the preprocessing time. Both simulation data and actual experiments have verified the effectiveness of the SPI-TV algorithm, which can reduce the impact of noise on reconstructed images while retaining structural edge information, suppress aliasing artifacts, and improve the quality of reconstructed images.