Image Super-Resolution using DWT and GMM

Jo Jan 31, 2025

Image super-resolution (SR) is the process of artificially producing a high-resolution (HR) image from one or several low-resolution (LR) images. Image super-resolution techniques are based on interpolation, reconstruction and learning.

In recent times, learning-based super-resolution algorithms are widely used. In learning-based super-resolution algorithms, HR image is obtained from a single LR image using training database. In these algorithms, the priori information is derived from the training database.

Ro Mi Ha, a lecturer at the Faculty of Information Science and Technology, has proposed a learning-based image super-resolution for a single LR image using discrete wavelet transform (DWT) and Gaussian mixture model (GMM).

In this method, if a low resolution (LR) input image and a database consisting of low and high resolution images are given, a high-resolution image for the input image is obtained by learning of the high-frequency details from the database. Then, high-frequency details of an HR image are described as wavelet coefficients at finer scale using DWT. The conversion function for obtaining the finer wavelet coefficients of an HR image from the coarse wavelet coefficients of an LR image is set as a weighted linear transformation using GMM.

She has demonstrated the effectiveness of the proposed method by conducting some experiments on gray images.

The proposed method can be used in applications such as remote surveillance where the memory, the transmission bandwidth and the camera performance are the main constraints.

Research on Temperature Distribution and Optimizati...

Jo Jan 30, 2025

The electrode support arm is the key component of an electric furnace, which supports the electrode, conducts electrical energy from the transformer through the electrode to the furnace, and produces a strong arc current within the furnace material to raise the temperature of the weld pool. The electrode support arm is usually mounted on the brace, and its front end holds the electrode by the gripper to keep it in position at the electrode center circle during the lift process.

During the operation, a high-temperature arc with a large power output is formed between the extremes of electrodes and the weld pool, which can cause intense electromagnetic oscillation of the electrode support arm. Therefore, the electrode support arm should have great strength and low resistance value. In modern electric furnaces, the secondary bus on the furnace body is retrofitted from the former copper tube bus to the copper-steel conductive electrode arm and aluminum conductive arm. To manufacture the copper-steel conductive electrode arm of a 5-ton ultrahigh power (UHP) electric furnace, a copper plate is welded around the steel structure and a cooling water pipe with a rectangular cross-section is formed on its bottom surface to cool the heat generated when the high current flows through the copper plate.

So far, many studies have been published on the cooling systems in the furnace body and furnace ceiling, but no mention has been made on the cooling in the electrode support arm section. The reason lies in the fact that Joule heat generated in the conductive arm is too small and only recently has the bimetallic conductive arm been widely used. However, during the reducing operation of the electric furnace, the temperature of the furnace should be raised to the maximum, so the operation is carried out without ventilation. Therefore, the convective heat transfer by this flue gas must be considered because the gas ambient temperature around the electrode support arm increases to about 700K.

Ri Sim Hyok, a researcher at the Faculty of Metal Engineering, has analyzed the temperature distribution of the copper-steel conductive electrode arm of UHP electric furnace, determined the geometry of the cooling water pipeline to minimize the cooling water consumption, while keeping the temperature of the conductive copper plate within the acceptable range (50℃), and determined the consumption amount of cooling water.

He analyzed the current flow in the bimetallic conductive arm using Maxwell software and found that the current flows only into the copper plate. Then, he simulated the temperature distribution of the bimetallic conductive arm during the reducing stroke with ANSYS FLUENT. After that, he simulated the temperature changes of the conductive copper plate and the cooling water, and verified the results with the experimental data obtained in situ.

The results show that the cooling water flow rate of 1 to 1.5kg/s and the 6-stroke pipeline guarantee the minimum consumption of cooling water while keeping the temperature of the copper plate bus of the copper-steel conductive electrode arm within the acceptable range.

Method of Probabilistic Document Ranking using Cons...

Jo Jan 29, 2025

A text can be represented by a vector of binary weights indicating the presence or absence of terms inside documents. Therefore, the correlation of terms is quantified by the frequency of co-occurrence or the internal product of the index term vector, but it has the limitation that it does not capture the joint relationship. As each element of the binary document vector is set to be 0 or 1, 0 and 1 are dealt with quantitatively and methods of multidimensional incidence tables and various statistics are used.

Maximum entropy is one method applicable to such problems, and it can represent a variety of models depending on which interaction terms are involved. The maximum entropy model is the basic principle of the maximum entropy classifier, and it constructs a model for all known factors and excludes the unknown factors. The basic characteristic is that the features are independent of each other, so the features useful to the final classification can be added freely, regardless of mutual influence.

The maximum entropy model with constraints on binary features is widely used in natural language processing, speaker identification, information retrieval, text filtering, machine translation, etc.

In lexeme equivocality removal using maximum entropy, when the context, i.e., neighboring words are given, the probability of each outcome (each meaning of the target word) is estimated and the variance of the neighboring words for each meaning is used to compute the probability. Either log-linear (log-linear) model or exponential (exponential) model is used to combine features. However, where the dependence structure between variables using maximum entropy is included, except for the relationship where the joint probability is zero, which would reduce the computational effort in training and prediction still remains a challenge.

Pak Il Chol, a researcher at the Faculty of Information Science and Technology, has proposed a joint distribution estimation method via marginal distribution constraint based on the maximum entropy principle in document data represented by binary weight vectors, and how to apply it to document classification and ranking. The proposed method allows determination of the target joint distribution using the marginal distribution obtained from the given data as a constraint. He has also proposed a method to search for the maximum dimension of the marginal distribution, except for the case of marginal probability 0.

The simulation results show that the proposed method for classification through marginal distribution estimation underperforms the simple Bayesian approach assuming conditional independence when the dataset size is small, but outperforms the simple Bayesian approach when the dataset size gets larger. He has also found that the ranking shows an improvement of about 4% in MAP over the two-valued independent retrieval model.