In this study, we developed a two-stage technology for improving the sharpness of images. In the first stage, the correction was performed using a linear square exponential (SE) filter with a centrally symmetric frequency response in the form of quadratic and exponential functions. This stage included setting the parameters of the SE filter and the actual processing. In the second stage, non-linear correction was carried out. The idea of the filter was to increase the impact of the central value, if it was at the edge of different intensity levels. We assumed that an increase in the absolute value of the weighted average of the differences in the point neighbourhood could be an indicator of such edges. The central point of the reference area belonged to the edge if its value was considerably greater or lesser than the significant number of values in this area. The first experiment confirmed the possibility for the improvement of the quantitative criteria of image restoration by non-linear correction. The second experiment illustrated the increase in the image sharpness obtained using a diffraction Fresnel lens. The proposed technology has opened up prospects for the use of cameras based on diffraction optic elements in mobile devices.
This paper addresses the problem of 3D scene reconstruction in cases when the extrinsic parameters (rotation and translation) of the camera are unknown. This problem is both important and urgent because the accuracy of the camera parameters significantly influences the resulting 3D model. A common approach is to determine the fundamental matrix from corresponding points on two views of a scene and then to use singular value decomposition for camera projection matrix estimation. However, this common approach is very sensitive to fundamental matrix errors. In this paper we propose a novel approach in which camera parameters are determined directly from the equations of the projective transformation by using corresponding points on the views. The proposed decomposition allows us to use an iterative procedure for determining the parameters of the camera. This procedure is implemented in two steps: the translation determination and the rotation determination. The experimental results of the camera parameters estimation and 3D scene reconstruction demonstrate the reliability of the proposed approach.