# Image Enhancement Techniques

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/image-enhancement-techniques/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the advancements in image enhancement techniques, including dehazing, contrast enhancement, and color transfer. It covers a wide range of topics such as underwater imaging, single image restoration, low-light enhancement, and high dynamic range imaging. The cluster showcases the application of deep learning methods in addressing challenges related to image processing and enhancement. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t11019 |
| Works | 92 |

## Topic papers all

Showing 15 of 92.

- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://scholariq.org/papers/perceptual-losses-for-real-time-style-transfer-and-super-resolution/)
- [Automatic Red-Channel underwater image restoration](https://scholariq.org/papers/automatic-red-channel-underwater-image-restoration/)
- [Spatial Attentive Single-Image Deraining With a High Quality Real Rain Dataset](https://scholariq.org/papers/spatial-attentive-single-image-deraining-with-a-high-quality-real-rain-dataset/)
- [Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation](https://scholariq.org/papers/whale-optimization-algorithm-and-moth-flame-optimization-for-multilevel/)
- [A Generalized Low-Rank Appearance Model for Spatio-temporally Correlated Rain Streaks](https://scholariq.org/papers/a-generalized-low-rank-appearance-model-for-spatio-temporally-correlated-rain/)
- [A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics](https://scholariq.org/papers/a-review-on-multimodal-medical-image-fusion-compendious-analysis-of-medical/)
- [Mutual Graph Learning for Camouflaged Object Detection](https://scholariq.org/papers/mutual-graph-learning-for-camouflaged-object-detection/)
- [Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study](https://scholariq.org/papers/advancing-image-understanding-in-poor-visibility-environments-a-collective/)
- [Research on data augmentation for image classification based on convolution neural networks](https://scholariq.org/papers/research-on-data-augmentation-for-image-classification-based-on-convolution/)
- [Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection](https://scholariq.org/papers/uncertainty-guided-transformer-reasoning-for-camouflaged-object-detection/)
- [Automatic Shadow Detection and Removal from a Single Image](https://scholariq.org/papers/automatic-shadow-detection-and-removal-from-a-single-image/)
- [Evaluation of tone mapping operators using a High Dynamic Range display](https://scholariq.org/papers/evaluation-of-tone-mapping-operators-using-a-high-dynamic-range-display/)
- [Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset](https://scholariq.org/papers/attention-guided-low-light-image-enhancement-with-a-large-scale-low-light/)
- [Inverse tone mapping](https://scholariq.org/papers/inverse-tone-mapping/)
- [Generalized Random Walks for Fusion of Multi-Exposure Images](https://scholariq.org/papers/generalized-random-walks-for-fusion-of-multi-exposure-images/)

## Topic primary papers

Showing 15 of 37.

- [Automatic Red-Channel underwater image restoration](https://scholariq.org/papers/automatic-red-channel-underwater-image-restoration/)
- [Spatial Attentive Single-Image Deraining With a High Quality Real Rain Dataset](https://scholariq.org/papers/spatial-attentive-single-image-deraining-with-a-high-quality-real-rain-dataset/)
- [A Generalized Low-Rank Appearance Model for Spatio-temporally Correlated Rain Streaks](https://scholariq.org/papers/a-generalized-low-rank-appearance-model-for-spatio-temporally-correlated-rain/)
- [Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study](https://scholariq.org/papers/advancing-image-understanding-in-poor-visibility-environments-a-collective/)
- [Evaluation of tone mapping operators using a High Dynamic Range display](https://scholariq.org/papers/evaluation-of-tone-mapping-operators-using-a-high-dynamic-range-display/)
- [Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset](https://scholariq.org/papers/attention-guided-low-light-image-enhancement-with-a-large-scale-low-light/)
- [Inverse tone mapping](https://scholariq.org/papers/inverse-tone-mapping/)
- [Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing](https://scholariq.org/papers/light-dehazenet-a-novel-lightweight-cnn-architecture-for-single-image-dehazing/)
- [An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm](https://scholariq.org/papers/an-optimum-multi-level-image-thresholding-segmentation-using-non-local-means-2d/)
- [Advanced High Dynamic Range Imaging](https://scholariq.org/papers/advanced-high-dynamic-range-imaging/)
- [NTIRE 2018 Challenge on Image Dehazing: Methods and Results](https://scholariq.org/papers/ntire-2018-challenge-on-image-dehazing-methods-and-results/)
- [Adaptive mammographic image enhancement using first derivative and local statistics](https://scholariq.org/papers/adaptive-mammographic-image-enhancement-using-first-derivative-and-local/)
- [Single-Image Dehazing via Optimal Transmission Map Under Scene Priors](https://scholariq.org/papers/single-image-dehazing-via-optimal-transmission-map-under-scene-priors/)
- [A local model of eye adaptation for high dynamic range images](https://scholariq.org/papers/a-local-model-of-eye-adaptation-for-high-dynamic-range-images/)
- [Haze and Thin Cloud Removal via Sphere Model Improved Dark Channel Prior](https://scholariq.org/papers/haze-and-thin-cloud-removal-via-sphere-model-improved-dark-channel-prior/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.