In this study, an efficient self‐learning method for image super‐resolution (SR) is presented. In the proposed algorithm, the input image is divided into equal size patches. Using these patches, a dictionary is learned based on K‐SVD, referred to as high resolution (HR) dictionary. Then, by down‐sampling, the columns of the dictionary, called atoms, a low resolution (LR) version of the dictionary is obtained. An initial estimate of the SR image is constructed using the bicubic interpolation on the input image. Then in an iterative algorithm, the difference between the down‐sampled version of the estimated SR image and the input image is obtained. This difference image, which includes reconstructed details is enlarged using sparse representation and LR/HR dictionaries. The enlarged detail is added to the latest reconstructed SR image. This process gradually improves the quality of the initial SR image. After several iterations, the reconstructed image is an SR version of the input image. Experimental results confirm that the proposed method performance is promising.

In this study, the authors propose a fast method for single image super‐resolution (SR). The relation between high‐resolution (HR)/low‐resolution (LR) patches is learned using the input image and a down‐sampled version. They divide the input image into a number of equal blocks. For each image block, a pair of HR/LR dictionaries, using informative patches, are constructed. For each patch in the input image, an HR dictionary is constructed by concatenating the HR dictionary which it belongs and the HR dictionaries of eight neighbouring blocks. In the same manner, an LR dictionary for each patch is constructed. They represent each patch from the input image using a linear combination of atoms in its LR dictionary. Then, using the same combination for atoms in the HR dictionary of patch, the SR version for the patch is constructed. The computational complexity of the proposed method is considerably low because, in contrast to most of the existing methods, no learning phase, for building the dictionaries of a patch, is required. The experimental results of the proposed method is significantly faster than existing methods, whereas the performance in terms of peak signal‐to‐noise ratio and structural similarity criterions is comparable with the existing methods.

Classification of remote sensing images from urban area as a means to achieve necessitated information for some applications such as automatic map updating and GIS, planning and emergency response has become one of the challenging subjects for image processing researches. In this paper, a method for classification of remote sensing image from urban area is addressed. First, motion based unsharp masking [MUSM] is applied to the input image to enhance its high frequency components. Then, laplacian of image as input feature for the Bayesian classifier is utilized. After that, size filter is used for large and small building discrimination. The classification of small and large building using unsharp mask and Bayesian discrimination function has increased in aspect of accuracy in comparison with original Bayesian method for classification of urban area. Experiments justify the efficiency of the proposed approach.

The success of many methods in fingerprint identification strongly depends on the accurate detection of singular points on the fingerprints. In this paper we propose a new approach for detecting the singular points. The method is based on measuring the maximal disturbance for direction of ridges in fingerprint images. We do not use the directional field in neighborhood of points directly so our method is comparatively fast. For this purpose we used 2D wavelet to detect high frequency components in three directions: horizontal, vertical and diagonal. The results of our experiments on different fingerprint databases confirm the ability of approach for rotation invariant, fast and accurate detection of singular points.

We propose a graph-based representation for the elliptic region shape descriptors introduced by Tuytelaars et al.13 In this representation we use image profiles to describe the relation between a pair of image regions. This new representation and a graph matching technique proposed in Ref. 1 are the basis of an object recognition method. An experimental comparative study between the original method and the new graph-based method is carried out. The results show that the graph-based method is more robust to scaling than the original method. Moreover, the misclassification rate using the graph-based method is considerably lower than that yielded by the original method.

An experimental comparative study between two representation methods for the recognition of 3D objects from a 2D view is carried out. The two methods compared are our ARG region-based representation [1] and the elliptic region-based method of Tuytelaars et al[9]. The results of the experiments conducted show that the former method outperforms the latter particularly under sever scaling and also when applied to objects with curved surfaces.

We address the problem of object recognition in computer vision. We rep-resent each model and the scene in the form of Attributed Relational Graph. A multiple region representation is provided at each node of the scene ARG to increase the representation reliability. The process of matching the scene ARG against the stored models is facilitated by a novel method for identi-fying the most probable representation from among the multiple candidates. The scene and model graph matching is accomplished using probabilistic relaxation which has been modified to minimise the label clutter. The exper-imental results obtained on real data demonstrate promising performance of the proposed recognition system. 1