# Exposure Interpolation by Combining Model-driven and Data-driven Methods

## TL;DR

This paper proposes a hybrid framework combining traditional and deep learning methods to improve exposure interpolation, demonstrating enhanced image quality and efficiency in processing large exposure ratio images.

## Contribution

It introduces a novel fusion framework that leverages both conventional and deep learning techniques for exposure interpolation, improving quality and convergence speed.

## Key findings

- Enhanced image quality of interpolated images.
- Reduced training data requirements for deep learning.
- Faster convergence of the hybrid method.

## Abstract

Deep learning based methods have penetrated many image processing problems and become dominant solutions to these problems. A natural question raised here is "Is there any space for conventional methods on these problems?" In this paper, exposure interpolation is taken as an example to answer this question and the answer is "Yes". A framework on fusing conventional and deep learning method is introduced to generate an medium exposure image for two large-exposureratio images. Experimental results indicate that the quality of the medium exposure image is increased significantly through using the deep learning method to refine the interpolated image via the conventional method. The conventional method can be adopted to improve the convergence speed of the deep learning method and to reduce the number of samples which is required by the deep learning method.