Because they are affected by imaging conditions, aliasing, noise, etc, imaging systems are unable to obtain all of the information contained in an original scene. Super-resolution (SR) reconstruction is important for the application of image data to increase the resolution of images. In this article, an example-based algorithm is proposed to implement SR reconstruction by single-image. The mapping function between low-resolution (LR) and high-resolution (HR) images is learned by using the method of regularized regression. Then, finding the optimal sparse subset of the training data set by kernel matching pursuit (KMP). The results show that this method can recover detailed information of images, and the computational cost is reduced compared to other example-based SR methods.
This paper presents an approach for estimating and then removing image radial distortion. It works on a single image and does not require a special calibration. The approach is extremely useful in many applications, particularly those where human-made environments contain abundant lines. A division model is applied, in which a straight line in the distorted image is treated as a circular arc. Levenberg–Marquardt (LM) iterative nonlinear least squares method is adopted to calculate the arc’s parameters. Then “Taubin fit” is applied to obtain the initial guess of the arc’s parameters which works as the initial input to the LM iteration. This dramatically improves the convergence rate in the LM process to obtain the required parameters for correcting image radial distortion. Hough entropy, as a measure, has achieved the quantitative evaluation of the estimated distortion based on the probability distribution in one-dimensional θ Hough space. The experimental results on both synthetic and real images have demonstrated that the proposed method can robustly estimate and then remove image radial distortion with high accuracy.