Results 21 to 30 of about 156,769 (258)
Multi-Feature Guided Low-Light Image Enhancement
Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even ...
Hong Liang +3 more
doaj +1 more source
Low-Light Hyperspectral Image Enhancement
Due to inadequate energy captured by the hyperspectral camera sensor in poor illumination conditions, low-light hyperspectral images (HSIs) usually suffer from low visibility, spectral distortion, and various noises. A range of HSI restoration methods have been developed, yet their effectiveness in enhancing low-light HSIs is constrained.
Xuelong Li 0001 +2 more
openaire +2 more sources
A Survey of Low-Light Image Enhancement
With the higher requirements of computer vision image enhancement of low-light image has become an important research content of computer vision. Traditional low-light image enhancement algorithms can improve image brightness and detailed visibility to varying degrees, but due to their strict mathematical derivation, such methods have bottlenecks and ...
Weiqiang Liu +3 more
openaire +2 more sources
Generative adversarial network for low‐light image enhancement
Low‐light image enhancement is rapidly gaining research attention due to the increasing demands of extreme visual tasks in various applications. Although numerous methods exist to enhance image qualities in low light, it is still undetermined how to ...
Fei Li +2 more
doaj +1 more source
Towards Robust Low Light Image Enhancement
In this paper, we study the problem of making brighter images from dark images found in the wild. The images are dark because they are taken in dim environments. They suffer from color shifts caused by quantization and from sensor noise. We don't know the true camera reponse function for such images and they are not RAW.
Sara Aghajanzadeh, David A. Forsyth
openaire +2 more sources
Hierarchical guided network for low‐light image enhancement
Due to insufficient illumination in low‐light conditions, the brightness and contrast of the captured images are low, which affect the processing of other computer vision tasks.
Xiaomei Feng, Jinjiang Li, Hui Fan
doaj +1 more source
Improved Retinex-Theory-Based Low-Light Image Enhancement Algorithm
Researchers working on image processing have had a hard time handling low-light images due to their low contrast, noise, and brightness. This paper presents an improved method that uses the Retinex theory to enhance low-light images, with a network model
Jiarui Wang +3 more
doaj +1 more source
Fusion‐based simultaneous estimation of reflectance and illumination for low‐light image enhancement
Low‐light image enhancement is a challenging field in image processing. Retinex‐based methods perform well for low‐light images. However, reflectance and illumination estimation is an ill‐posed problem.
Anil Singh Parihar +3 more
doaj +1 more source
Lighting the darkness in the sea: A deep learning model for underwater image enhancement
Currently, optical imaging cameras are widely used on underwater vehicles to obtain images and support numerous marine exploration tasks. Many underwater image enhancement algorithms have been proposed in the past few years to suppress backscattering ...
Yaofeng Xie +5 more
doaj +1 more source
Unsupervised Low-Light Image Enhancement in the Fourier Transform Domain
Low-light image enhancement is an important task in computer vision. Deep learning-based low-light image enhancement has made significant progress. But the current methods also face the challenge of relying on a wide variety of low-light/normal-light ...
Feng Ming, Zhihui Wei, Jun Zhang
doaj +1 more source

