# Fully Convolutional Network for Removing DCT Artefacts From Images

## TL;DR

This paper introduces three fully convolutional network models designed to effectively reduce compression artifacts like blockiness and ringing in lossy compressed images, improving image quality.

## Contribution

It presents novel FCN architectures tailored for artifact removal and investigates the impact of initialization strategies on reconstruction quality.

## Key findings

- Models improve image quality by reducing artifacts
- Predefined filter initialization enhances reconstruction performance
- Processing similar to compression algorithm benefits artifact removal

## Abstract

Image compression is one of the essential methods of image processing. Its most prominent advantage is the significant reduction of image size allowing for more efficient storage and transfer. However, lossy compression is associated with the loss of some image details in favor of reducing its size. In compressed images, the deficiencies are manifested by noticeable defects in the form of artifacts; the most common are block artifacts, ringing effect, or blur. In this article, we propose three models of fully convolutional networks with different configurations and examine their abilities in reducing compression artifacts. In the experiments, we research the extent to which the results are improved for models that will process the image in a similar way to the compression algorithm, and whether the initialization with predefined filters would allow for better image reconstruction than developed solely during learning.