A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution

Effective aggregation of temporal information of consecutive frames is the core of achieving video super-resolution. Many scholars have utilized structures such as sliding windows and recurrent to gather spatio-temporal information of frames. However, although the performance of the constructed VSR models is improving, the size of the models is also increasing, exacerbating the demand on the equipment. Thus, to reduce the stress on the device, we propose a novel lightweight recurrent grouping attention network. The parameters of this model are only 0.878M, which is much lower than the current mainstream model for studying video super-resolution. We design forward feature extraction module and backward feature extraction module to collect temporal information between consecutive frames from two directions. Moreover, a new grouping mechanism is proposed to efficiently collect spatio-temporal information of the reference frame and its neighboring frames. The attention supplementation module is presented to further enhance the information gathering range of the model. The feature reconstruction module aims to aggregate information from different directions to reconstruct high-resolution features. Experiments demonstrate that our model achieves state-of-the-art performance on multiple datasets

Interpolation algorithms with image structure preservation

Predmet istraživanja ove doktorske disertacije je problem interpolacije slike. Glavni fokus disertacije je interpolacija slike uz očuvanje prirodnosti teksture i očuvanje ivica (oštrine) interpolirane slike. Dodatni izazov je da algoritam za interpolaciju slike bude pogodan za primenu u uređajima sa ograničenim resursima. Kvalitet rešenja se ocenjuje poređenjem sa algoritmima poznatim u dostupnoj literaturi korišćenjem odgovarajućih metrika.This PhD dissertation addresses the problem of image interpolation. The main focus of the dissertation is image interpolation algorithm which preserves edges and keeps a natural texture of interpolated images. Additional challenge for image interpolation algorithm is to be suitable for application on resourcelimited platforms. The quality of the proposed solution is benchmarked against known image interpolation algorithms using appropriate metrics