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Reinforcement Learning for Intelligent Image Restoration: A Survey on Algorithms, Applications, and Engineering Challenges
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Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions
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Deep reinforcement learning in computer vision: a comprehensive survey
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Image registration optimization mechanism based on reinforcement learning and real time denoising
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The Pascal Visual Object Classes (VOC) Challenge
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1007/s11263-024-02168-7openalex-snapshot
Exploiting Diffusion Prior for Real-World Image Super-Resolution
inferredcites →https://doi.org/10.1007/s11263-025-02570-9openalex-snapshot
Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey
inferredcites →https://doi.org/10.1007/s11760-020-01824-yopenalex-snapshot
Mixed distortion image enhancement method based on joint of deep residuals learning and reinforcement learning
inferredcites →https://doi.org/10.1016/j.jvcir.2018.01.012openalex-snapshot
RENOIR – A dataset for real low-light image noise reduction
inferredcites →https://doi.org/10.1016/j.media.2014.11.010openalex-snapshot
Assessment of algorithms for mitosis detection in breast cancer histopathology images
inferredcites →https://doi.org/10.1016/j.neucom.2022.02.046openalex-snapshot
A survey of deep learning approaches to image restoration
inferredcites →https://doi.org/10.1016/j.patcog.2022.109046openalex-snapshot
Deep learning for image inpainting: A survey
inferredcites →https://doi.org/10.1016/j.patrec.2024.04.019openalex-snapshot
Low-quality image object detection based on reinforcement learning adaptive enhancement
inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1109/access.2023.3262751openalex-snapshot
Image Enhancement Using Adaptive Region- Guided Multi-Step Exposure Fusion Based on Reinforcement Learning
inferredcites →https://doi.org/10.1109/joe.2022.3140563openalex-snapshot
Underwater Image Enhancement by Attenuated Color Channel Correction and Detail Preserved Contrast Enhancement
inferredcites →https://doi.org/10.1109/joe.2022.3152519openalex-snapshot
Underwater Image Enhancement With Reinforcement Learning
inferredcites →https://doi.org/10.1109/joe.2022.3223733openalex-snapshot
Underwater Image Enhancement Method via Multi-Interval Subhistogram Perspective Equalization
inferredcites →https://doi.org/10.1109/lsp.2012.2227726openalex-snapshot
Making a “Completely Blind” Image Quality Analyzer
inferredcites →https://doi.org/10.1109/tcsvt.2019.2920407openalex-snapshot
Image De-Raining Using a Conditional Generative Adversarial Network
inferredcites →https://doi.org/10.1109/tcsvt.2019.2963772openalex-snapshot
Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light
inferredcites →https://doi.org/10.1109/tcsvt.2023.3290363openalex-snapshot
Non-Uniform Illumination Underwater Image Restoration via Illumination Channel Sparsity Prior
inferredcites →https://doi.org/10.1109/tgrs.2022.3227548openalex-snapshot
GIFM: An Image Restoration Method With Generalized Image Formation Model for Poor Visible Conditions
inferredcites →https://doi.org/10.1109/tip.2013.2261309openalex-snapshot
Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images
inferredcites →https://doi.org/10.1109/tip.2015.2500021openalex-snapshot
Massive Online Crowdsourced Study of Subjective and Objective Picture Quality
inferredcites →https://doi.org/10.1109/tip.2016.2598681openalex-snapshot
DehazeNet: An End-to-End System for Single Image Haze Removal
inferredcites →https://doi.org/10.1109/tip.2016.2631888openalex-snapshot
Waterloo Exploration Database: New Challenges for Image Quality Assessment Models
inferredcites →https://doi.org/10.1109/tip.2017.2662206openalex-snapshot
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
inferredcites →https://doi.org/10.1109/tip.2018.2839891openalex-snapshot
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
inferredcites →https://doi.org/10.1109/tip.2018.2867951openalex-snapshot
Benchmarking Single-Image Dehazing and Beyond
inferredcites →https://doi.org/10.1109/tip.2019.2955241openalex-snapshot
An Underwater Image Enhancement Benchmark Dataset and Beyond
inferredcites →https://doi.org/10.1109/tip.2021.3051462openalex-snapshot
EnlightenGAN: Deep Light Enhancement Without Paired Supervision
inferredcites →https://doi.org/10.1109/tip.2023.3286263openalex-snapshot
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators
inferredcites →https://doi.org/10.1109/tmi.2024.3419809openalex-snapshot
STAR-RL: Spatial-Temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution
inferredcites →https://doi.org/10.1109/tmm.2019.2960636openalex-snapshot
PixelRL: Fully Convolutional Network With Reinforcement Learning for Image Processing
inferredcites →https://doi.org/10.1109/tpami.2021.3096255openalex-snapshot
Path-Restore: Learning Network Path Selection for Image Restoration
inferredcites →https://doi.org/10.1109/tpami.2024.3355988openalex-snapshot
PathNet: Path-Selective Point Cloud Denoising
inferredcites →https://doi.org/10.1145/3532625openalex-snapshot
Image Quality Assessment–driven Reinforcement Learning for Mixed Distorted Image Restoration
inferredcites →https://doi.org/10.1609/aaai.v34i01.5423openalex-snapshot
MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning
inferredcites →https://doi.org/10.1609/aaai.v34i07.6790openalex-snapshot
Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software
inferredcites →https://doi.org/10.1609/aaai.v38i5.28307openalex-snapshot
RL-SeqISP: Reinforcement Learning-Based Sequential Optimization for Image Signal Processing
inferredcites →https://doi.org/10.3390/s22134707openalex-snapshot
A Survey of Deep Learning-Based Image Restoration Methods for Enhancing Situational Awareness at Disaster Sites: The Cases of Rain, Snow and Haze
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