Essentially, while the spatial matrix captures movement through space, the temporal matrix captures changes over time, with the object's position held constant. The disadvantage of this method is that the acquisition speed of the camera must be synchronized with the translation speed of the object. Otherwise, the reconstruction will not be possible. With the algorithm proposed in this article, it is possible to obtain a correct reconstruction of the object even if the acquisition speed of the camera and the translation speed are not synchronized. In a production environment, changing the camera acquisition speed or the linear motion is not always possible. When creating reconstructed images in thermography and hyperspectral inspections, the motion of the sample and the frame rate of the camera are synchronized. In this way, it is relatively easy to extract the desired area from each frame of the recorded sequence,as described in [23]. In industry, however, it is rarely possible to match the movement speed of the object to the frame rate of a camera, as this would result in fewer objects being produced per hour. Cameras, on the other hand, often have a fixed frame rate, making it impossible to adjust the speed at which an object moves. In this manuscript, a novel algorithm is proposed for reconstructing images acquired with an unsynchronized dynamic measurement. The current literature features several papers on dynamic line-scanning thermography, yet our work introduces a novel aspect. Our method uniquely allows the camera and object speed to operate asynchronously, eliminating the need for upfront synchronization. The process is facilitated by our innovative algorithm, a technique not previously explored according to our knowledge. This advancement significantly contributes to the field by offering enhanced flexibility and efficiency in thermographic imaging. This algorithm can be used for dynamic line-scan thermography measurements, enabling us to detect defects in objects that are hidden beneath the surface, while the object is moving. Essential for an industrial process. This algorithm also provides a solution for measurements with a heterogeneous illumination source, as is often the case when using HSI or UV cameras. An illumination source is difficult to obtain in industrial environments due to safety or financial reasons. Essentially, it employs the same algorithm and reconstructs an image of a specific spatial location that has an even illumination, thus facilitating defect detection. It is possible to image samples in multiple sections and then stitch them together to examine the entire sample with sufficient resolution. This method only works if the light source used is homogeneous. The slightest difference in illumination will cause a deviation in the recorded data, making it pointless to stitch the different sections together. This is illustrated in Figure 1, where the individual images are stitched together to reconstruct the original object. In this article, we first explain in detail the algorithm used, followed by a description of the equipment used for the measurements. In Section 3, the algorithm is applied to three different camera technologies: UV, IR and RGB, demonstrating the usefulness of this novel algorithm.