A Study on Edge Enhancement Using Transposed Convolution

In Computer Vision, Super-Resolution is a key technique used to convert low-resolution images into high- resolution images. Traditional bicubic interpolation-based methods suffer from detail loss and exhibit limitations in restoring edges and textures. In this paper, we propose a method that optimizes upscaling filters using a learnable transposed convolution and applies subsequent convolutional layers to restore fine details.The proposed method performs direct upscaling using a single transposed convolution layer, while subsequent convolutional layers are designed to minimize checkerboard artifacts and correct pixel imbalance. Additionally, Super-Resolution Convolutional Neural Network is applied in the final stage to enhance fine structures and further improve image quality. Experimental results demonstrate that our model outperforms conventional bicubic interpolation and standard transposed convolution methods in terms of Peak Signal-to-Noise Ratio and Structural Similarity Index Measure, producing visually sharper results. This study is expected to provide an effective solution for Super-Resolution image reconstruction and various Computer Vision applications.