# An Effective Image Restorer: Denoising and Luminance Adjustment for Low-photon-count Imaging

arXiv: https://arxiv.org/abs/2110.15715 · HTML: https://mlsanity.com/paper/2110.15715/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 16 of 16)

- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration](https://mlsanity.com/paper/1508.02848/) (2015)
- [Real Image Denoising with Feature Attention](https://mlsanity.com/paper/1904.07396/) (2019)
- [A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising](https://mlsanity.com/paper/1807.04364/) (2018)
- [Non-Local Color Image Denoising with Convolutional Neural Networks](https://mlsanity.com/paper/1611.06757/) (2016)
- [Dynamic Low-light Imaging with Quanta Image Sensors](https://mlsanity.com/paper/2007.08614/) (2020)
- [HDR Imaging with Quanta Image Sensors: Theoretical Limits and Optimal Reconstruction](https://mlsanity.com/paper/2011.03614/) (2020)
- [Megapixel Photon-Counting Color Imaging using Quanta Image Sensor](https://mlsanity.com/paper/1903.09036/) (2019)
- [Optimal Threshold Design for Quanta Image Sensor](https://mlsanity.com/paper/1704.03886/) (2017)

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

- [LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images](https://mlsanity.com/paper/2209.02197/) (2022)

## Abstract (arXiv)

Imaging under photon-scarce situations introduces challenges to many applications as the captured images are with low signal-to-noise ratio and poor luminance. In this paper, we investigate the raw image restoration under low-photon-count conditions by simulating the imaging of quanta image sensor (QIS). We develop a lightweight framework, which consists of a multi-level pyramid denoising network (MPDNet) and a luminance adjustment (LA) module to achieve separate denoising and luminance enhancement. The main component of our framework is the multi-skip attention residual block (MARB), which integrates multi-scale feature fusion and attention mechanism for better feature representation. Our MPDNet adopts the idea of Laplacian pyramid to learn the small-scale noise map and larger-scale high-frequency details at different levels, and feature extractions are conducted on the multi-scale input images to encode richer contextual information. Our LA module enhances the luminance of the denoised image by estimating its illumination, which can better avoid color distortion. Extensive experimental results have demonstrated that our image restorer can achieve superior performance on the degraded images with various photon levels by suppressing noise and recovering luminance and color effectively.