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Works278 from public data
- Automatic Contrast-Limited Adaptive Histogram Equalization With Dual Gamma Correction319
Since automatic CLAHE adaptively enhances contrast in each block while boosting luminance, it is very effective in enhancing dark images and daylight ones with strong dark shadows and outperforms state-of-the-art methods in terms of visual quality and quantitative measures.
- GBDT-MO: Gradient-Boosted Decision Trees for Multiple Outputs236
This article proposes a general method to learn gradient-boosted decision trees for multiple outputs, called GBDT-MO, which achieves outstanding performance in terms of accuracy, training speed, and inference speed.
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- PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report161
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones and proposes solutions that significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
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- Image deblocking via sparse representation119
Experimental results indicate that the proposed deblocking method is very effective in dealing with the image deblocking problem from compressed images.
- A stroke filter and its application to text localization77
A stroke filter which can detect strokes of texts for robust text localization and remove text candidates which have strong edges but are not text is proposed.
- Retinex-Based Perceptual Contrast Enhancement in Images Using Luminance Adaptation66
Experimental results demonstrate that the proposed retinex-based perceptual contrast enhancement in images using luminance adaptation successfully enhances details in an image while preventing over and under enhancement as well as outperforms state of the arts in terms of various quantitative measurements.
- Low light image enhancement with dual-tree complex wavelet transform63
An effective contrast enhancement method based on dual-tree complex wavelet transform (DT-CWT) which operates on a wide range of imagery without noise amplification and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction is proposed.
- Head pose estimation using deep neural networks and 3D point clouds61
The proposed method is lightweight and computationally efficient by utilizing a sampled 3D point cloud as input combined with a graph convolutional neural network (GCNN) and achieves outstanding performance in head pose estimation.
- Edge and identity preserving network for face super-resolution58
A novel Edge and Identity Preserving Network for Face Super-Resolution Network, named as EIPNet, which minimizes the distortion by utilizing a lightweight edge block and identity information and defines an identity loss function to preserve identification of super-resolved images.
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- NIR to RGB Domain Translation Using Asymmetric Cycle Generative Adversarial Networks44
Experimental results show that the proposed method achieves natural looking NIR colorization results with high generalization ability, and outperforms state-of-the-art ones in realistic colorization and resistance to unregistration.
- Siamese Dense Network for Reflection Removal with Flash and No-Flash Image Pairs41
The proposed siamese dense network (SDN) for reflection removal with flash and no-flash image pairs outperforms state-of-the-art ones in terms of visual quality and quantitative measurements and applies to color/depth image pairs and achieves both color reflection removal and depth filling.
- Accurate text localization in images based on SVM output scores41
A new approach for accurate text localization in images based on SVM (support vector machine) output scores that achieves impressive performance with respect to the accuracy, robustness and efficiency.
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- A new approach for text segmentation using a stroke filter39
This paper attempts to consider the intrinsic characteristics of the text by using the stroke filter and design a new and robust algorithm for text segmentation using a stroke filter based on local region analysis.
- Perceptual Enhancement of Low Light Images Based on Two-Step Noise Suppression38
Experimental results show that the proposed method perceptually enhances contrast in low-light images while successfully minimizing distortions and preserving details, and performs perceptual noise suppression using the JND model.
- FinerPCN: High fidelity point cloud completion network using pointwise convolution37
Experimental results show that FinerPCN generates finer detailed completion results than state-of-the-art methods while successfully keeping the shape of the input.
- Fusionnet: Multispectral Fusion of RGB and NIR Images Using Two Stage Convolutional Neural Networks36
Experimental results show that the proposed method produces natural looking fusion results by successfully recovering colors and textures, and outperforms state-of-the-art fusion methods in terms of visual quality and quantitative measurements.
- Automatic cardiac MRI segmentation and permutation-invariant pathology classification using deep neural networks and point clouds33
The proposed method successfully segments LV, RV, and Myo from cardiac MRI images and achieves comparable results against several existing ones, and the CDN successfully classifies heart diseases based on point clouds and achieves 92% accuracy on the testing dataset.
- Eye detection under varying illumination using the retinex theory33
A novel framework for illumination invariant eye detection under varying lighting conditions using adaptive smoothing based on the retinex theory and the support vector machine (SVM) classification for eye verification is proposed.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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