Image restoration is a classic problem in image processing. Image degradations can occur due to several reasons, for instance, imperfections of imaging systems, quantization errors, atmospheric turbulence, relative motion between camera or objects, among others. Motion blur is a typical degradation in dynamic imaging systems. In this work, we present a method to estimate the parameters of linear motion blur degradation from a captured blurred image. The proposed method is based on analyzing the frequency spectrum of a captured image in order to firstly estimate the degradation parameters, and then, to restore the image with a linear filter. The performance of the proposed method is evaluated by processing synthetic and real-life images. The obtained results are characterized in terms of accuracy of image restoration given by an objective criterion.
The perspective and lens distortions induced by the imaging system of a camera device are corrected by using
an elementary geometrical approach. We propose a simple method based on the use of a crossed grating in the
reference plane and a phase demodulation process. Preliminary results showing the performance of the proposed
method are discussed.
Scale estimation of objects is a challenging problem in image processing. This work presents a novel method to detect and estimate the scaling factor of a target in an observed scene corrupted with additive noise and clutter. Given a set of available views of the target the proposed method is able to detect the target and estimate its scaling factor using a template matched filters and a scale pyramidal representation. The performance of the proposed method is evaluated in synthetic and real-life scenes in different pattern recognition applications. The obtained results are characterized in terms of objective metrics.
A performance evaluation of several state-of-the-art correlation filters within the context of target tracking is presented. The filters are tested using an introduced algorithm that is adapted online using information of current and past scene frames of the scene. The algorithm achieves a high-rate operation by focusing signal processing on a small fragment of the scene in each frame. The correlation filters are tested using several video test sequences that contain geometric modifications of the target, partial occlusions and clutter. The performance of the tested filters is characterized in terms of detection efficiency, tracking accuracy, and computational complexity using objective metrics.
A reliable method for real-time target tracking is presented. The method is based on an interest point detector and a bank of locally adaptive correlation filters. The point detector is used to identify local regions in the observed scene around potential location of the target. The bank of correlation filters is employed to reliably detect the target and accurately estimate its position within the scene, by processing the local regions identified by the detector. Using information of past state estimates of the target the proposed algorithm predicts the state of the target in the next frame in order to perform a fast and accurate target tracking by focusing signal processing only on small regions of the scene in each frame. In order to achieve a real-time operation performance the proposed algorithm is implemented in a graphics processing unit. Experimental results obtained with the proposed method are presented, discussed, and compared with those obtained with a similar state-of-the-art target tracking algorithm.