the goal of multi-frame super resolution (sr) is to fuse multiple low resolution (lr) images to produce one high resolution (hr) image. the major challenge of classic sr approaches is accurate motion estimation between the frames. to handle this challenge, fuzzy motion estimation method has been proposed that replaces value of each pixel using the weighted averaging all its neighboring pixels i...

This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks...

Contrast resolution and detail resolution are two important parameters in ultrasound imaging. This paper presents a new method to enhance these parameters, simultaneously. A parallel auxiliary beamformer has been employed whose weightings are such that an estimation of the leaked signal through the main beamformer is obtained. Then the output of main beamformer is modified according to the ...

In this paper, an approach to improve the spatial resolution of color video is presented. Such high resolution images are desired, for example, in video printing. Previous work has shown that the most important step in achieving high quality results is the accuracy of the motion eld. It is well known that motion estimation is an ill-posed problem. However, in processing color video, additional ...

The evaluation of uncertainties and errors is an important topic in introductory physics and chemistry laboratory courses. Errors are usually classified into two groups depending on their source: systematic or bias errors, and random or statistical errors. The first group includes, for instance, miscalibrated scales or bad zero settings. They can be avoided if the meter is checked against a sta...

This paper proposes a learning scheme based still image super-resolution reconstruction algorithm. Superresolution reconstruction is proposed as a binary classification problem and can be solved by conditional class probability estimation. Assuming the probability takes the form of additive logistic regression function, AdaBoost algorithm is used to predict the probability. Experiments on face ...