# Advanced Image Processing Techniques

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

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the development and application of single image super-resolution techniques, utilizing deep learning methods such as convolutional networks, generative adversarial networks, and sparse representation. The cluster also covers related topics such as deblurring, video enhancement, and applications in medical imaging. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t11105 |
| Works | 79 |

## Topic papers all

Showing 15 of 79.

- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://scholariq.org/papers/perceptual-losses-for-real-time-style-transfer-and-super-resolution/)
- [Non-local sparse models for image restoration](https://scholariq.org/papers/non-local-sparse-models-for-image-restoration/)
- [Automatic Red-Channel underwater image restoration](https://scholariq.org/papers/automatic-red-channel-underwater-image-restoration/)
- [Feedback Network for Image Super-Resolution](https://scholariq.org/papers/feedback-network-for-image-super-resolution/)
- [aLow-dose CT via convolutional neural network](https://scholariq.org/papers/alow-dose-ct-via-convolutional-neural-network/)
- [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/)
- [Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward](https://scholariq.org/papers/deepfakes-generation-and-detection-state-of-the-art-open-challenges/)
- [Burst Denoising with Kernel Prediction Networks](https://scholariq.org/papers/burst-denoising-with-kernel-prediction-networks/)
- [Deep learning for deepfakes creation and detection: A survey](https://scholariq.org/papers/deep-learning-for-deepfakes-creation-and-detection-a-survey/)
- [Temporal Generative Adversarial Nets with Singular Value Clipping](https://scholariq.org/papers/temporal-generative-adversarial-nets-with-singular-value-clipping-2/)
- [Unsupervised Degradation Representation Learning for Blind Super-Resolution](https://scholariq.org/papers/unsupervised-degradation-representation-learning-for-blind-super-resolution/)
- [CogView: Mastering Text-to-Image Generation via Transformers](https://scholariq.org/papers/cogview-mastering-text-to-image-generation-via-transformers/)
- [NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results](https://scholariq.org/papers/ntire-2018-challenge-on-single-image-super-resolution-methods-and-results/)
- [A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation](https://scholariq.org/papers/a-survey-on-deep-learning-techniques-for-stereo-based-depth-estimation/)
- [Learning Parallax Attention for Stereo Image Super-Resolution](https://scholariq.org/papers/learning-parallax-attention-for-stereo-image-super-resolution/)

## Topic primary papers

Showing 15 of 25.

- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://scholariq.org/papers/perceptual-losses-for-real-time-style-transfer-and-super-resolution/)
- [Feedback Network for Image Super-Resolution](https://scholariq.org/papers/feedback-network-for-image-super-resolution/)
- [Unsupervised Degradation Representation Learning for Blind Super-Resolution](https://scholariq.org/papers/unsupervised-degradation-representation-learning-for-blind-super-resolution/)
- [NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results](https://scholariq.org/papers/ntire-2018-challenge-on-single-image-super-resolution-methods-and-results/)
- [Learning Parallax Attention for Stereo Image Super-Resolution](https://scholariq.org/papers/learning-parallax-attention-for-stereo-image-super-resolution/)
- [Exploring Sparsity in Image Super-Resolution for Efficient Inference](https://scholariq.org/papers/exploring-sparsity-in-image-super-resolution-for-efficient-inference/)
- [Spatial-Angular Interaction for Light Field Image Super-Resolution](https://scholariq.org/papers/spatial-angular-interaction-for-light-field-image-super-resolution/)
- [Coupled Dictionary and Feature Space Learning with Applications to Cross-Domain Image Synthesis and Recognition](https://scholariq.org/papers/coupled-dictionary-and-feature-space-learning-with-applications-to-cross-domain/)
- [A Simple Local Minimal Intensity Prior and an Improved Algorithm for Blind Image Deblurring](https://scholariq.org/papers/a-simple-local-minimal-intensity-prior-and-an-improved-algorithm-for-blind-image/)
- [Convolutional Neural Network-Based Block Up-Sampling for Intra Frame Coding](https://scholariq.org/papers/convolutional-neural-network-based-block-up-sampling-for-intra-frame-coding/)
- [Deep Learning-Based Video Coding](https://scholariq.org/papers/deep-learning-based-video-coding/)
- [Learning a Convolutional Neural Network for Image Compact-Resolution](https://scholariq.org/papers/learning-a-convolutional-neural-network-for-image-compact-resolution/)
- [Space-Time Super-Resolution Using Graph-Cut Optimization](https://scholariq.org/papers/space-time-super-resolution-using-graph-cut-optimization/)
- [Deep Learning-Based Video Coding: A Review and A Case Study](https://scholariq.org/papers/deep-learning-based-video-coding-a-review-and-a-case-study/)
- [Enhancing wind power monitoring through motion deblurring with modified GoogleNet algorithm](https://scholariq.org/papers/enhancing-wind-power-monitoring-through-motion-deblurring-with-modified/)

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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.