Results 181 to 190 of about 36,775 (242)

Image dehazing algorithm based on deep transfer learning and local mean adaptation. [PDF]

Sci Rep
Shi D, Huang S.
europepmc   +1 more source

Hyperopic keratorefractive lenticule extraction. [PDF]

J Cataract Refract Surg
Fu D, Wei R, Shi M, Peng X, Zhou X, Han T.   +5 more
europepmc   +1 more source

Contrastive Learning-Driven Image Dehazing with Multi-Scale Feature Fusion and Hybrid Attention Mechanism. [PDF]

J Imaging
Zhang H, Wang J, Tu X, Niu Z, Wang Y.
europepmc   +1 more source

Comparative Analysis of Corneal Densitometry Changes Following Standard Versus Accelerated Corneal Cross-Linking Protocol. [PDF]

Medicina (Kaunas)
Šklebar LK, Jandroković S, Kalauz M, Škegro I, Lešin Gaćina D, Bešlić I, Gabrić I, Tomljanović N, Mrvelj B, Vidas Pauk S.   +9 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Single Image Haze Removal With Haze Map Optimization for Various Haze Concentrations

IEEE Transactions on Circuits and Systems for Video Technology, 2022
Hazy images suffer from poor visibility and possess low-contrast, degrading the visibility of the scene. The performance of single-image haze removal methods is limited by priors or constraints. This paper presents an efficient single image dehazing method by cascading two models.
Biswarup Ganguly, Anwesa Bhattacharya, Ananya Srivastava, Debangshu Dey, Sugata Munshi   +4 more
openaire   +1 more source

Variational Single Nighttime Image Haze Removal With a Gray Haze-Line Prior

IEEE Transactions on Image Processing, 2022
Influenced by glowing effects, nighttime haze removal is a challenging ill-posed task. Existing nighttime dehazing methods usually result in glowing artifacts, color shifts, overexposure, and noise amplification. Thus, through statistical and theoretical analyses, we propose a simple and effective gray haze-line prior (GHLP) to identify accurate hazy ...
Wenhui Wang, Anna Wang, Chen Liu
openaire   +2 more sources

Dense Haze Removal by Nonlinear Transformation

IEEE Transactions on Circuits and Systems for Video Technology, 2022
Images captured in hazy or foggy weather conditions, suffer from various problems, such as limited visibility, low contrast, color distortions. These images are used in many computer vision applications, such as video surveillance, transportation, remote sensing.
Subhash Chand Agrawal, Anand Singh Jalal
openaire   +1 more source

A simple haze removal algorithm to remove haze from videos

2017 International Conference on Computing Methodologies and Communication (ICCMC), 2017
Outdoor images or videos taken in hazy climate often get degraded due to the effect of haze. There are several methods to remove haze from hazy images. Most of them oversaturates the dehazed images. This degrades the quality of images. The colour attenuation prior technique is one of the best algorithm to remove haze from images.
P. B Karthika, R. Sindhu
openaire   +1 more source

Haze Identification and Classification Model for Haze Removal Techniques

2021
In the modern age, several applications depend on the information collected from the images and its classification. However, due to the poor climatic conditions, the images captured by the camera are degraded in terms of contrast, brightness, color, and sharpness, which create hazy images.
Gaurav Saxena, Sarita Singh Bhadauria
openaire   +1 more source