# Metropolis Theorem and Its Applications in Single Image Detail Enhancement

arXiv: https://arxiv.org/abs/2302.09762 · HTML: https://mlsanity.com/paper/2302.09762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [Joint Image Filtering with Deep Convolutional Networks](https://mlsanity.com/paper/1710.04200/) (2017)
- [Fast End-to-End Trainable Guided Filter](https://mlsanity.com/paper/1803.05619/) (2018)
- [Aesthetic-Driven Image Enhancement by Adversarial Learning](https://mlsanity.com/paper/1707.05251/) (2017)
- [A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing](https://mlsanity.com/paper/1907.09642/) (2019)
- [Structure-preserving Guided Retinal Image Filtering and Its Application for Optic Disc Analysis](https://mlsanity.com/paper/1805.06625/) (2018)
- [Decouple Learning for Parameterized Image Operators](https://mlsanity.com/paper/1807.08186/) (2018)
- [Zero-order Reverse Filtering](https://mlsanity.com/paper/1704.04037/) (2017)
- [Real-time Image Smoothing via Iterative Least Squares](https://mlsanity.com/paper/2003.07504/) (2020)
- [A Benchmark for Edge-Preserving Image Smoothing](https://mlsanity.com/paper/1904.01579/) (2019)
- [Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing](https://mlsanity.com/paper/1812.07122/) (2018)
- [Contrastive Semantic-Guided Image Smoothing Network](https://mlsanity.com/paper/2209.00977/) (2022)

## Built on by (most cited first, 1 of 1)

- [From Noise to Nuance: Advances in Deep Generative Image Models](https://mlsanity.com/paper/2412.09656/) (2024)

## Abstract (arXiv)

Traditional image detail enhancement is local filter-based or global filter-based. In both approaches, the original image is first divided into the base layer and the detail layer, and then the enhanced image is obtained by amplifying the detail layer. Our method is different, and its innovation lies in the special way to get the image detail layer. The detail layer in our method is obtained by updating the residual features, and the updating mechanism is usually based on searching and matching similar patches. However, due to the diversity of image texture features, perfect matching is often not possible. In this paper, the process of searching and matching is treated as a thermodynamic process, where the Metropolis theorem can minimize the internal energy and get the global optimal solution of this task, that is, to find a more suitable feature for a better detail enhancement performance. Extensive experiments have proven that our algorithm can achieve better results in quantitative metrics testing and visual effects evaluation. The source code can be obtained from the link.