This study focused on developing software for image reconstruction system of optical image data obtained from four projections of CMOS linear image sensors by using MATLAB. Four projections of collimated light beams at least must be used in order to avoid aliasing and smear effect that may be appeared on the reconstructed image obtained. The image reconstruction is based on linear back-projection method where transpose matrix, and pseudo-inverse matrix are used to solve inverse matrix problems in MATLAB. Results were compared between both inverse problem calculation methods selected. It was discovered that transpose matrix method performs better than pseudo–inverse matrix for a high resolution images. A graphical user interface has been implemented, it is capable to reconstruct image from raw data collected from sensor. Reconstruction results demonstrated in most cases are able to produce an adequate image for further analysis by user.

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This paper presents reconstruction of objects using multiple-views of 2D images. As a first step to get familiar in research field, it is simply done with several MATLAB image processing tools while acquisition of data is captured by a digital camera where the object statically stands in front of a cardboard as a background. The digital camera used is a mobile phone camera which it is fixed at a place and captures 36 different angles of views in order to get projection images for more accurate shape can be reconstructed. The 36 views are obtained using a piece of paper with reference angle drew on it in order to rotate the object. There are several image processing techniques applied on the images to reconstruction them as a 3D image which are thresholding, Radon transform, inverse Radon transform and also edge detection. The results obtained show that 3D reconstruction has successfully created where it is good enough to reconstruct exactly same shape as the original object.

This paper presents a simple computation method to reconstruct 3-dimensional (3D) model from a sequence of 2-dimensional (2D) images using a multiple-view camera setup. The 3D model is acquired by applying several images processing on few 2D images captured by digital camera with different angle of views. The setup for this study consisted of a digital camera mounted on a tripod stand focusing at a block of model object on a turntable with black floor and background. 36 different angles are used to capture the images where every view angle differs by ten degree (10°) with another view in a fixed sequence. The image processing applied on all 2D images to be reconstructed as 3D surface are image segmentation, Radon transform (RT), image filtering, morphological operation, edge detection, and boundary extraction. The results for 3D model reconstruction shows it is well reconstructed, with a smooth texture obtained using 3D mesh and Delaunay triangulation, while the shape is nearly identical to the original model while the remaining are distinguishable.