Name: Dat Ngo
Type: User
Company: Department of Computer Engineering, Korea National University of Transportation
Bio: Assistant Professor, Department of Computer Engineering, Korea National University of Transportation, South Korea.
Location: South Korea
Blog: https://datngo93.github.io/datngo/
Dat Ngo's Projects
Personal site
This is a MATLAB source code of the enhanced equidistribution, which guarantees that the generated random sequence follows the theoretical uniform distribution.
Automatically exported from code.google.com/p/fpga-cam
This is the MATLAB implementation of the haziness degree evaluator for predicting the haze density from a single image. The relevant work was published in the MDPI Sensors journal under the title "Haziness degree evaluator: a knowledge-driven approach for haze density estimation".
Just for fun!
This source code is a MATLAB implementation of a haze removal algorithm that can deal with the post-dehazing false enlargement of white objects effectively. The work was published in MDPI Sensors journal under the title "Single-Image Visibility Restoration: A Machine Learning Approach and Its 4K-Capable Hardware Accelerator".
This is a MATLAB source code of the paper "Improved Color Attenuation Prior for Single-Image Haze Removal", published in Applied Sciences-Basel (MDPI).
This is the MATLAB source code of a haze removal algorithm, which dehazes a hazy input image using simple image enhancement techniques, such as detail enhancement, gamma correction, and single-scale image fusion.
This source code is a MATLAB implementation of a nonlinear unsharp masking method, published in the proceeding of ICEIC 2020, Barcelona, Spain. The algorithm was implemented by means of generalized operators, therein lies the underlying cause of its robustness against out-of-range issue.
This is the MATLAB source code of a haze removal algorithm published in Remote Sensing (MDPI) under the title "Robust Single-Image Haze Removal Using Optimal Transmission Map and Adaptive Atmospheric Light". The transmission map was estimated by maximizing an objective function quantifying image contrast and sharpness. Additionally, an adaptive atmospheric light was devised to prevent the loss of dark details after removing haze.