# Convolutional Neural Networks Considering Local and Global features for Image Enhancement

## TL;DR

This paper introduces a CNN architecture that effectively combines local and global features for improved image enhancement, trained on HDR data to restore details lost in low-quality images.

## Contribution

The novel CNN architecture integrates local and global feature extraction modules, enhancing image quality beyond existing CNN-based methods.

## Key findings

- Outperforms conventional methods in objective quality metrics
- Utilizes HDR images for superior training data
- Produces higher-quality images with better detail restoration

## Abstract

In this paper, we propose a novel convolutional neural network (CNN) architecture considering both local and global features for image enhancement. Most conventional image enhancement methods, including Retinex-based methods, cannot restore lost pixel values caused by clipping and quantizing. CNN-based methods have recently been proposed to solve the problem, but they still have a limited performance due to network architectures not handling global features. To handle both local and global features, the proposed architecture consists of three networks: a local encoder, a global encoder, and a decoder. In addition, high dynamic range (HDR) images are used for generating training data for our networks. The use of HDR images makes it possible to train CNNs with better-quality images than images directly captured with cameras. Experimental results show that the proposed method can produce higher-quality images than conventional image enhancement methods including CNN-based methods, in terms of various objective quality metrics: TMQI, entropy, NIQE, and BRISQUE.