Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Philippe BekaertSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works73 from public data
- 1,347
- 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.
- 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.
- 140
- 137
- 136
- 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.
- 99
- 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.
- 75
- 68
- 64
- 59
- 58
- 56
- 51
- 45
- 45
- 31
- 29
- 29
- 23
- 23
- 16
- 14
- 12
- 11
- 10
- 9
- 8
- 7
- 7
- 6
- 5
- 5
- 4
- 4
- 3
- 3
- 3
- 2
- 2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.