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- Philippe BekaertSuggested from co-authorship
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Works90 from public data
- 1,347
- 1,142
- O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images781
The first outdoor scenes database composed of pairs of real hazy and corresponding haze-free images is introduced, and is used to compare a representative set of state-of-the-art dehazing techniques, using traditional image quality metrics such as PSNR, SSIM and CIEDE2000.
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- NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images499
This work presents an objective assessment of several state- of-the-art single image dehazing methods that were evaluated using NH-HAZE dataset, a non-homogeneous realistic dataset with pairs of real hazy and corresponding haze-free images.
- Dense-Haze: A Benchmark for Image Dehazing with Dense-Haze and Haze-Free Images499
Dense-Haze dataset aims to push significantly the state-of-the-art in single-image dehazing by promoting robust methods for real and various hazy scenes and contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes.
- I-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Indoor Images482
A new dataset -named I-HAZE- that contains 35 image pairs of hazy and corresponding haze-free (ground-truth) indoor images that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM.
- 345
- A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image231
A novel approach to restore a single image degraded by atmospheric phenomena such as fog or haze is introduced, based on an extensive study on a large data set of images, and validated based on a metric that measures the contrast but also the structural changes.
- Color Channel Compensation (3C): A Fundamental Pre-Processing Step for Image Enhancement207
The proposed approach, called Color Channel Compensation (3C), overcomes artifacts resulting from the severely non-uniform color spectrum distribution encountered in images captured under hazy night-time conditions, underwater, or under non- uniform artificial illumination and is shown to consistently improve the outcome of conventional restoration methods.
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- LYT-NET: Lightweight YUV Transformer-Based Network for Low-Light Image Enhancement127
This letter introduces LYT-Net, a novel lightweight transformer-based model for low-light image enhancement that outperforms recent LLIE methods.
- NTIRE 2020 Challenge on NonHomogeneous Dehazing125
This paper reviews the NTIRE 2020 Challenge on Non-Homogeneous Dehazing of images (restoration of rich details in hazy image) and proposed solutions gauge the state-of-the-art in image dehazing.
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- Day and Night-Time Dehazing by Local Airlight Estimation89
An effective fusion-based technique to enhance both day-time and night-time hazy scenes by estimating the airlight on image patches and not on the entire image using a Laplacian pyramid decomposition.
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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 2026-10-13. 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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