# Enhancing Underexposed Photos using Perceptually Bidirectional Similarity

## TL;DR

This paper introduces a novel method for enhancing underexposed photos by ensuring perceptual consistency through a bidirectional similarity criterion, leading to artifact-free, high-quality images and videos.

## Contribution

It proposes a new perceptually bidirectional similarity criterion and formulates enhancement as a constrained illumination estimation problem, improving over existing methods.

## Key findings

- Outperforms state-of-the-art enhancement methods
- Effectively reduces visual artifacts like color distortion and detail loss
- Extends to underexposed video enhancement with consistent illumination propagation

## Abstract

Although remarkable progress has been made, existing methods for enhancing underexposed photos tend to produce visually unpleasing results due to the existence of visual artifacts (e.g., color distortion, loss of details and uneven exposure). We observed that this is because they fail to ensure the perceptual consistency of visual information between the source underexposed image and its enhanced output. To obtain high-quality results free of these artifacts, we present a novel underexposed photo enhancement approach that is able to maintain the perceptual consistency. We achieve this by proposing an effective criterion, referred to as perceptually bidirectional similarity, which explicitly describes how to ensure the perceptual consistency. Particularly, we adopt the Retinex theory and cast the enhancement problem as a constrained illumination estimation optimization, where we formulate perceptually bidirectional similarity as constraints on illumination and solve for the illumination which can recover the desired artifact-free enhancement results. In addition, we describe a video enhancement framework that adopts the presented illumination estimation for handling underexposed videos. To this end, a probabilistic approach is introduced to propagate illuminations of sampled keyframes to the entire video by tackling a Bayesian Maximum A Posteriori problem. Extensive experiments demonstrate the superiority of our method over the state-of-the-art methods.