Enhancing Image Super-Resolution with Convolutional Neural Network Ensembles
Priya Guna · 2023
The goal of image super-resolution (SR) is to increase the resolution and quality of low-resolution images, and it is a crucial problem in computer vision. Recent years have seen incredible progress in SR problems using Convolutional Neural Networks (CNNs). However, boosting SR performance even further remains difficult. Using the strength of Convolutional Neural Network ensembles, this research introduces a new method for image super-resolution. By pooling the advantages of several different CNN models, we may produce an ensemble that is more accurate and robust than any of the individual models. Here, we present a flexible ensemble framework for integrating multiple SR designs into a single system for comprehensive problem-solving. We show that our ensemble approach improves image super-resolution outcomes through a thorough experimental assessment on industry-standard benchmark datasets. We also explore other methods for training and fusing the ensemble members, such as consensus-based methods and adaptive weighting schemes. To achieve optimal SR performance, these methods aim to collect synergistic data from multiple independently operating networks. Our ensemble-based method not only achieves state-of-the-art super-resolution results, but it also shows resistance to noise and other degradation factors. To further illuminate the process of image super-resolution, we also offer some information on the interpretability of ensemble decisions. this research offers a fresh and efficient approach to improving image super-resolution using ensembles of Convolutional Neural Networks. Our method not only improves the current state of the art in SR, but it also points to a promising way for making super-resolution methods more robust and reliable in real-world settings.