SOTAVerified

Image Enhancement

Image Enhancement is basically improving the interpretability or perception of information in images for human viewers and providing ‘better’ input for other automated image processing techniques. The principal objective of Image Enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer.

Source: A Comprehensive Review of Image Enhancement Techniques

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 651–700 of 983 papers

TitleStatusHype
Performance Evaluation of Infrared Image Enhancement Techniques—0
Photo-unrealistic Image Enhancement for Subject Placement in Outdoor Photography—0
Photovoltaic module segmentation and thermal analysis tool from thermal images—0
Semi-supervised Underwater Image Enhancement Using A Physics-Aware Triple-Stream Network—0
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report—0
Positron Emission Tomography (PET) image enhancement using a gradient vector orientation based nonlinear diffusion filter (GVOF) for accurate quantitation of radioactivity concentration—0
PQDynamicISP: Dynamically Controlled Image Signal Processor for Any Image Sensors Pursuing Perceptual Quality—0
Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks—0
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement—0
Projection image-to-image translation in hybrid X-ray/MR imaging—0
Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging—0
Properties on n-dimensional convolution for image deconvolution—0
Pyramid Texture Filtering—0
Quality Assessment for Comparing Image Enhancement Algorithms—0
Quality Enhancement of Radiographic X-ray Images by Interpretable Mapping—0
Quantitative and Qualitative Evaluation of NLM and Wavelet Methods in Image Enhancement—0
Quantum Annealing for Single Image Super-Resolution—0
Quarter Laplacian Filter for Edge Aware Image Processing—0
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution—0
Rain Removal and Illumination Enhancement Done in One Go—0
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—0
RB-Dust -- A Reference-based Dataset for Vision-based Dust Removal—0
Realistic Hair Synthesis with Generative Adversarial Networks—0
Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables—0
Real-world Instance-specific Image Goal Navigation: Bridging Domain Gaps via Contrastive Learning—0
JoReS-Diff: Joint Retinex and Semantic Priors in Diffusion Model for Low-light Image Enhancement—0
Recognition-Oriented Low-Light Image Enhancement based on Global and Pixelwise Optimization—0
Reconstruction-based Pairwise Depth Dataset for Depth Image Enhancement Using CNN—0
Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learning—0
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications—0
Relative Learning from Web Images for Content-adaptive Enhancement—0
Remote Sensing Image Enhancement through Spatiotemporal Filtering—0
Removal of Salt and Pepper noise from Gray-Scale and Color Images: An Adaptive Approach—0
Research on Tumors Segmentation based on Image Enhancement Method—0
Retaining Image Feature Matching Performance Under Low Light Conditions—0
Rethinking Model Redundancy for Low-light Image Enhancement—0
Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework—0
Revealing Shadows: Low-Light Image Enhancement Using Self-Calibrated Illumination—0
RSEND: Retinex-based Squeeze and Excitation Network with Dark Region Detection for Efficient Low Light Image Enhancement—0
RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement—0
S2MS: Self-Supervised Learning Driven Multi-Spectral CT Image Enhancement—0
SALVE: Self-supervised Adaptive Low-light Video Enhancement—0
SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistency—0
SDI-Net: Toward Sufficient Dual-View Interaction for Low-light Stereo Image Enhancement—0
SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild—0
Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement—0
Seeing Objects in dark with Continual Contrastive Learning—0
Seeing Text in the Dark: Algorithm and Benchmark—0
Seeing Through the Noisy Dark: Towards Real-world Low-Light Image Enhancement and Denoising—0
Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur—0
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All datasetsMIT-Adobe 5kTIP 2018Exposure-ErrorsMIT-Adobe FiveKSICE-GradSICE-Mix

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HG-MTFEPSNR on proRGB25.69—Unverified
2PQDynamicISPPSNR on proRGB25.53—Unverified
3AdaIntPSNR on proRGB25.49—Unverified
4RSFNet-mapPSNR on proRGB25.49—Unverified
5SepLUTPSNR on proRGB25.47—Unverified
6MTFEPSNR on proRGB25.46—Unverified
73D LUTPSNR on proRGB25.21—Unverified
8RetinexformerPSNR on sRGB24.94—Unverified
94D LUTPSNR on proRGB24.61—Unverified
10DIFAR (MSCA, level 1)PSNR on proRGB24.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ESDNet-LPSNR30.11—Unverified
2MBCNNPSNR30.03—Unverified
3ESDNetPSNR29.81—Unverified
4Uformer-BPSNR29.28—Unverified
5MopNetPSNR27.75—Unverified
6DMCNNPSNR26.77—Unverified
#ModelMetricClaimedVerifiedStatus
1Exposure-slotPSNR23.18—Unverified
2CSECPSNR22.73—Unverified
3LCDPNetPSNR22.17—Unverified
4IATPSNR20.34—Unverified
5MSECPSNR20.21—Unverified
#ModelMetricClaimedVerifiedStatus
1TreEnhanceDeltaE11.25—Unverified
#ModelMetricClaimedVerifiedStatus
1CIDNetAverage PSNR13.45—Unverified
#ModelMetricClaimedVerifiedStatus
1CIDNetAverage PSNR13.43—Unverified