Том 29, № 2 (2019)

Blind motion deblurring is a highly challenging inverse problem in image processing and low-level computer vision. In this paper, we propose a novel approach to identify the parameters (blur length and orientation) of motion blur from an observed image. The kernel estimation is based on a novel criterion — the minimization of a blurred Stein’s unbiased risk estimate (blur-SURE): an unbiased estimate of a filtered mean squared error. By incorporating a simple Wiener filtering into the blur-SURE, the motion blur is estimated by minimizing this new objective functional with high accuracy. We then perform non-blind deconvolution using the high-quality SURE-LET algorithm with the estimated kernel. The results of synthetic and real experiments are quite competitive with other state-of-the-art algorithms under a wide range of degradation scenarios both numerically and visually.

Fingerprints are considered as the oldest and most widely used in the world for biometric identification. Every person has unique and permanent fingerprints. Most automatic fingerprint recognition systems are based on features formed from lines known as minutiae. Building a database of unique minutiae is very important in the security systems because it concerns the identification of the person committing a crime through the latent left in the crime scene. This research presents a new algorithm to give a minutia a unique value leading to accelerating the search process for a person. The algorithm splits the fingerprint image into four sections, then calculates the values for each minutia in each section and stores it in a database that is designed for this purpose. This research provides a great opportunity and additional options to fingerprint experts in order to solve many cases that are still undiscovered while searching for the latent in the database of minutiae. The results of testing this algorithm were very successful, very encouraging and helpful to fingerprint experts in their work.

This article is devoted to resolving problems with synthesis of FIR-filter restoring distortions such as defocusing. We propose a new parametrical class of finite impulse response filters (FIR-filters) based on a model of the one-dimensional radially-symmetric frequency response. In the proposed synthesis method, the one-dimensional frequency response was composed of square and exponential functions. The two-dimensional filter’s impulse response (SE-filter) was constructed by sampling one-dimensional impulse responses for all directions. For filter parameters estimation, the iterative scheme was used. We considered two approaches to estimating the filter parameters: with training images and in the absence of training images. The important advantages of the proposed synthesis method are the possibility of high quality restoration with a minimum of prior information and the low amount of computing resources required. Given examples illustrates the possibility of the high-quality distortion correction. The proposed method provides higher quality restorations than optimal Wiener filter (from Open CV) due to a lack of distortion on the border of images.

The aim of the present study is to analysing the effect of water absorption on iron ore samples in the performances of SVM-based machine vision system. Two types of SVM-based machine vision system (classification and regression) were designed and developed, and performances were compared with dry and wet ore sample images. The images of the ore samples were captured in both the conditions (wet and dry) to examine the proposed model performance. A total of 280 image features were extracted and optimised using sequential forward floating selection (SFFS) algorithm for model development. The iron ore samples were collected from an Indian iron ore mine (Guamine), and image capturing system was fabricated in the laboratory for executing the proposed study. The results indicated that a different set of optimised features obtained for dry and wet sample images in both the models (classification and regression). Furthermore, the performance of both the models with dry sample images was found to be relatively better than the wet sample images.