# Deep Decomposition and Bilinear Pooling Network for Blind Night-Time Image Quality Evaluation

arXiv: https://arxiv.org/abs/2205.05880 · HTML: https://mlsanity.com/paper/2205.05880/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 68 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Bilinear CNNs for Fine-grained Visual Recognition](https://mlsanity.com/paper/1504.07889/) (2015)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks](https://mlsanity.com/paper/1704.02470/) (2017)
- [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017)
- [GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment](https://mlsanity.com/paper/2103.07666/) (2021)
- [A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement](https://mlsanity.com/paper/1711.00591/) (2017)
- [Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement](https://mlsanity.com/paper/1911.11323/) (2019)
- [Recurrent Exposure Generation for Low-Light Face Detection](https://mlsanity.com/paper/2007.10963/) (2020)
- [Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data](https://mlsanity.com/paper/1904.08632/) (2019)
- [Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image](https://mlsanity.com/paper/1909.11983/) (2019)
- [Efficient No-Reference Quality Assessment and Classification Model for Contrast Distorted Images](https://mlsanity.com/paper/1804.02554/) (2018)

## Abstract (arXiv)

Blind image quality assessment (BIQA), which aims to accurately predict the image quality without any pristine reference information, has been extensively concerned in the past decades. Especially, with the help of deep neural networks, great progress has been achieved. However, it remains less investigated on BIQA for night-time images (NTIs) which usually suffers from complicated authentic distortions such as reduced visibility, low contrast, additive noises, and color distortions. These diverse authentic degradations particularly challenges the design of effective deep neural network for blind NTI quality evaluation (NTIQE). In this paper, we propose a novel deep decomposition and bilinear pooling network (DDB-Net) to better address this issue. The DDB-Net contains three modules, i.e., an image decomposition module, a feature encoding module, and a bilinear pooling module. The image decomposition module is inspired by the Retinex theory and involves decoupling the input NTI into an illumination layer component responsible for illumination information and a reflection layer component responsible for content information. Then, the feature encoding module involves learning feature representations of degradations that are rooted in the two decoupled components separately. Finally, by modeling illumination-related and content-related degradations as two-factor variations, the two feature sets are bilinearly pooled together to form a unified representation for quality prediction. The superiority of the proposed DDB-Net has been well validated by extensive experiments on several benchmark datasets. The source code will be made available soon.