Discarding jagged artefacts in image upscaling with total variation regularisation

Abstract

Image upscaling is needed in many areas. There are two types of methods: methods based on a simple hypothesis and methods based on machine learning. Most of the machine learning‐based methods have disadvantages: no support is provided for a variety of upscaling factors, a training process with a high time cost is required, and a large amount of storage space and high‐end equipment are required. To avoid the disadvantages of machine learning, upscaling images with a simple hypothesis is a promising strategy but simple hypothesis always produces jaggy artifacts. The authors propose a new method to remove these jagged artifacts. They consider an edge in an image as a deformed curve. Removing jagged artefacts is considered equivalent to shortening the full arc length of a curve. By optimising the regularization model, the severity of the artifacts decreases as the number of iterations increases. They compare nine existing methods on the Set5, Set14, and Urban100 datasets. Without using any external data, the proposed algorithm has high visual quality, has few jagged artefacts and performs similarly to very recent state‐of‐the‐art deep convolutional network‐based approaches. Compared to other methods without external data, the proposed algorithm balances the quality and time cost well.

Image Upscaling with Deep Machine Learning for Energy-Efficient Data Communications

“…Total variation regularization pursues minimizing the total variation over pixels; meanwhile, allowing insignificant or little changes to be made. That is, the method fulfills smoothing the change of pixel values while it still retains edges [15].…”