Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex

Tian, Feng; Wang, Mengjiao; Liu, Xiaopei

doi:10.3390/app14052213

Cited by 7 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A multi-scale Retinex with color restoration (MSRCR) technique is suggested to address image degradation under foggy weather conditions [31]. An enhanced Retinex algorithm is proposed for enhancing low-light images, addressing issues such as local halo blurring [32].…”

Section: Traditional Enhancement Methodsmentioning

confidence: 99%

LLE-NET: A Low-Light Image Enhancement Algorithm Based on Curve Estimation

Cao,

2024

Mathematics

View full text Add to dashboard Cite

Low-light image enhancement is very significant for vision tasks. We introduce Low-light Image Enhancement via Deep Learning Network (LLE-NET), which employs a deep network to estimate curve parameters. Cubic curves and gamma correction are employed for enhancing low-light images. Our research trains a lightweight network to estimate the parameters that determine the correction curve. By the results of the deep learning network, accurate correction curves are confirmed, which are used for the per-pixel correction of RGB channels. The image enhanced by our models closely resembles the input image. To further accelerate the inferring speed of the low-light enhancement model, a low-light enhancement model based on gamma correction is proposed with one iteration. LLE-NET exhibits remarkable inference speed, achieving 400 fps on a single GPU for images sized 640×480×3 while maintaining pleasing enhancement quality. The enhancement model based on gamma correction attains an impressive inference speed of 800 fps for images sized 640×480×3 on a single GPU.

show abstract

Section: Traditional Enhancement Methodsmentioning

confidence: 99%

LLE-NET: A Low-Light Image Enhancement Algorithm Based on Curve Estimation

Cao,

2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…H(x) is utilized to assess the amount of information in the image. A higher entropy value indicates a more uniform distribution of pixel values, resulting in a higher level of texture and detail in the image [24].…”

Section: Measure Of Entropy (Moe)mentioning

confidence: 99%

Research on the Contrast Enhancement Algorithm for X-ray Images of BiFeO3 Material Experiment

Li,

Yu,

Pan

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

High-Temperature Materials Science Experiment Cabinet on the Chinese Space Station is mainly used to carry out experimental research related to high-temperature materials science in microgravity. It is equipped with an X-ray transmission imaging module, which is applied to realize transmission imaging of material samples under microgravity. However, the X-ray light source is far away from the experimental samples, and the images obtained by the module are blurred, so it is impossible to accurately observe the morphological changes during the melting and solidification processes of high-temperature materials. To address this issue, this paper proposed a contrast enhancement algorithm specifically designed for X-ray images obtained during the experiments of high-temperature materials. The algorithm is based on gradient three-interval equalization, and it is combined with a Gaussian function to expand the gradient histogram. Meanwhile, the local gray level information within each gradient interval is corrected by designing an improved adaptive contrast enhancement algorithm. By comparing with Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms, EnlightenGAN, and Wavelet algorithms, the Contrast Enhancement based contrast-changed Image Quality measure (CEIQ) and Measure of Enhancement (EME) are improved by an average of 56.97%, 10.58%, and Measure of Entropy (MOE) are improved by an average of 7.74 times. The experimental results show that the algorithm makes the image details clearer on the basis of image contrast enhancement. The solid-liquid interface in the image can be clearly observed after contrast enhancement. The algorithm provides strong support for the study of interface dynamics during the experiment process of high-temperature materials.

show abstract

“…Guided filtering is a filtering method based on the local linear model [20]. It assumes that any pixel has a certain linear relationship with the pixels in its local neighborhood.…”

Section: Multi-scale Guided Filteringmentioning

confidence: 99%

Thermal Infrared-Image-Enhancement Algorithm Based on Multi-Scale Guided Filtering

Li,

Wang,

et al. 2024

Fire

View full text Add to dashboard Cite

Obtaining thermal infrared images with prominent details, high contrast, and minimal background noise has always been a focal point of infrared technology research. To address issues such as the blurriness of details and low contrast in thermal infrared images, an enhancement algorithm for thermal infrared images based on multi-scale guided filtering is proposed. This algorithm fully leverages the excellent edge-preserving characteristics of guided filtering and the multi-scale nature of the edge details in thermal infrared images. It uses multi-scale guided filtering to decompose each thermal infrared image into multiple scales of detail layers and a base layer. Then, CLAHE is employed to compress the grayscale and enhance the contrast of the base layer image. Then, detail-enhancement processing of the multi-scale detail layers is performed. Finally, the base layer and the multi-scale detail layers are linearly fused to obtain an enhanced thermal infrared image. Our experimental results indicate that, compared to other methods, the proposed method can effectively enhance image contrast and enrich image details, and has higher image quality and stronger scene adaptability.

show abstract

Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex

Cited by 7 publications

References 26 publications

LLE-NET: A Low-Light Image Enhancement Algorithm Based on Curve Estimation

LLE-NET: A Low-Light Image Enhancement Algorithm Based on Curve Estimation

Research on the Contrast Enhancement Algorithm for X-ray Images of BiFeO3 Material Experiment

Thermal Infrared-Image-Enhancement Algorithm Based on Multi-Scale Guided Filtering

Contact Info

Product

Resources

About