earticle

영어

Recently, Implicit Neural Representations (INRs) have been gaining attention as an approach where neural networks learn a continuous function that takes coordinates as input and outputs the color values at those locations. Using INRs allows for reconstructing images of any size without constraints on spatial resolution. As a result, it has emerged as a promising method for super resolution, enabling a single neural network to represent images at all resolutions. However, existing research on INR-based super-resolution still lags behind other deep learning methods in terms of performance. This is because a single neural network, which takes uniform coordinate values as input, faces challenges in representing information of varying complexity across different regions of an image. Therefore, we propose a method to improve super-resolution performance by decomposing an image into variable-sized blocks so that each block has uniform complexity, regardless of the regional variation in complexity. The INR neural network then learns the image information of each block with uniform complexity. By alleviating differences in regional complexity, the neural network is able to learn regional information more stably and accurately, enabling optimal performance even in areas with diverse levels of complexity.