Fabric defects inspection is a vital step for fabric quality assessment. Many vision-based automatic fabric defect detection methods have been proposed to detect fabric flaws efficiently and accurately. Because the inspection methods are vision-based, image quality is of great importance to the accuracy of detection result. To our knowledge, most of camera lenses have radial distortion. So our goal in this project is to remove the radial distortion and achieve undistorted images. Much research work has been done for 2-D image correction, but the study for 1-D line scan camera image correction is rarely done, although line scan cameras are gaining more and wider applications due to the high resolution and efficiency on 1-D data processing. A novel line scan camera correction method is proposed in this project. We first propose a pattern object with mutually parallel lines and oblique lines to each pair of parallel ones. The purpose of the pattern design is based upon the fact that line scan camera acquires image one line at a time and it's difficult for one scan line to match the "0-D" marked points on pattern. We detect the intersection points between pattern lines and one scan line and calculate their position according to the pattern geometry. As calibrations for 2-D cameras have been greatly achieved, we propose a method to calibrate 1-D camera. A least-square method is applied to solve the pinhole projection equation and estimate the values of camera parameter matrix. Finally we refine the data with maximum-likelihood estimation and get the camera lens distortion coefficients. We re-project the data from the image coordinate to the world coordinate, using the obtained camera matrix and the re-projection error is 0.68 pixel. With the distortion coefficients ready, we correct captured images with an undistortion equation. We introduce a term of unit distance in the discussion part to better assess the proposed method. When testifying the undistortion results, we observe corrected image has almost identical unit distance with standard deviation of 0.29 pixels. Compared to the ideal distortion-free unit distance, the corrected image has only 0.09 pixel off the average, which proves the validity of the proposed method. / text