scholarly journals Retinex-Based Multiphase Algorithm for Low-Light Image Enhancement

2020 ◽  
Vol 37 (5) ◽  
pp. 733-743
Author(s):  
Mohammad Abid Al-Hashim ◽  
Zohair Al-Ameen
Keyword(s):  
Image Enhancement ◽  
Processing Speed ◽  
Sigmoid Function ◽  
Light Effect ◽  
Low Light ◽  
Single Scale ◽  
Retinex Algorithm ◽  
Adequate Quality ◽  
Normalization Function

These days, digital images are one of the most profound methods used to represent information. Still, various images are obtained with a low-light effect due to numerous unavoidable reasons. It may be problematic for humans and computer-related applications to perceive and extract valuable information from such images properly. Hence, the observed quality of low-light images should be ameliorated for improved analysis, understanding, and interpretation. Currently, the enhancement of low-light images is a challenging task since various factors, including brightness, contrast, and colors should be considered effectively to produce results with adequate quality. Therefore, a retinex-based multiphase algorithm is developed in this study, in that it computes the illumination image somewhat similar to the single-scale retinex algorithm, takes the logs of both the original and the illumination images, subtract them using a modified approach, the result is then processed by a gamma-corrected sigmoid function and further processed by a normalization function to produce to the final result. The proposed algorithm is tested using natural low-light images, evaluated using specialized metrics, and compared with eight different sophisticated methods. The attained experiential outcomes revealed that the proposed algorithm has delivered the best performances concerning processing speed, perceived quality, and evaluation metrics.

Perceptual Quality Assessment of Low-light Image Enhancement

2021 ◽  
Vol 17 (4) ◽  
pp. 1-24
Author(s):  
Guangtao Zhai ◽  
Wei Sun ◽  
Xiongkuo Min ◽  
Jiantao Zhou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Low Light ◽  
Light Image ◽  
Light Enhancement

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

scholarly journals Image Enhancement and Denoising in Extreme Low-Light Conditions

2019 ◽  
Vol 9 (1) ◽  
pp. 5259-5264
Keyword(s):  
Image Enhancement ◽  
Smart Phone ◽  
Image Data ◽  
Structural Similarity ◽  
Noise Removal ◽  
Low Light ◽  
Daily Experience ◽  
Quantitative Results ◽  
Loss Term

Image noise refers to the specks of false colors or artifacts that diminish the visual quality of the captured image. It has become our daily experience that with affordable smart-phone cameras we can capture high clarity photos in a brightly illuminated scene. But using the same camera in a poorly lit environment with high ISO settings results in images that are noisy with irrelevant specks of colors. Noise removal and contrast enhancement in images have been extensively studied by researchers over the past few decades. But most of these techniques fail to perform satisfactorily if the images are captured in an extremely dark environment. In recent years, computer vision researchers have started developing neural network-based algorithms to perform automated de-noising of images captured in a low-light environment. Although these methods are reasonably successful in providing the desired de-noised image, the transformation operation tends to distort the structure of the image contents to a certain extent. We propose an improved algorithm for image enhancement and de-noising using the camera’s raw image data by employing a deep U-Net generator. The network is trained in an end-to-end manner on a large training set with suitable loss functions. To preserve the image content structures at a higher resolution compared to the existing approaches, we make use of an edge loss term in addition to PSNR loss and structural similarity loss during the training phase. Qualitative and quantitative results in terms of PSNR and SSIM values emphasize the effectiveness of our approach.

scholarly journals A Smart System for Low-Light Image Enhancement with Color Constancy and Detail Manipulation in Complex Light Environments

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 718 ◽  
Author(s):  
Ziaur Rahman ◽  
Muhammad Aamir ◽  
Yi-Fei Pu ◽  
Farhan Ullah ◽  
Qiang Dai
Keyword(s):  
Image Enhancement ◽  
Noise Suppression ◽  
Weighted Least Squares ◽  
Color Constancy ◽  
Low Light ◽  
Illumination Estimation ◽  
Proposed Model ◽  
Visual Artifacts ◽  
True Color

Images are an important medium to represent meaningful information. It may be difficult for computer vision techniques and humans to extract valuable information from images with low illumination. Currently, the enhancement of low-quality images is a challenging task in the domain of image processing and computer graphics. Although there are many algorithms for image enhancement, the existing techniques often produce defective results with respect to the portions of the image with intense or normal illumination, and such techniques also inevitably degrade certain visual artifacts of the image. The model use for image enhancement must perform the following tasks: preserving details, improving contrast, color correction, and noise suppression. In this paper, we have proposed a framework based on a camera response and weighted least squares strategies. First, the image exposure is adjusted using brightness transformation to obtain the correct model for the camera response, and an illumination estimation approach is used to extract a ratio map. Then, the proposed model adjusts every pixel according to the calculated exposure map and Retinex theory. Additionally, a dehazing algorithm is used to remove haze and improve the contrast of the image. The color constancy parameters set the true color for images of low to average quality. Finally, a details enhancement approach preserves the naturalness and extracts more details to enhance the visual quality of the image. The experimental evidence and a comparison with several, recent state-of-the-art algorithms demonstrated that our designed framework is effective and can efficiently enhance low-light images.

Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function

IJOSTHE ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 8
Author(s):  
Puspad Kumar Sharma ◽  
Nitesh Gupta ◽  
Anurag Shrivastava
Keyword(s):  
Input Image ◽  
Sigmoid Function ◽  
Single Image ◽  
Low Light ◽  
Lighting Condition ◽  
Low Contrast ◽  
General Terms ◽  
Degraded Image ◽  
Result Analysis

Due to camera resolution or any lighting condition, captured image are generally over-exposed or under-exposed conditions. So, there is need of some enhancement techniques that improvise these artifacts from recorded pictures or images. So, the objective of image enhancement and adjustment techniques is to improve the quality and characteristics of an image. In general terms, the enhancement of image distorts the original numerical values of an image. Therefore, it is required to design such enhancement technique that do not compromise with the quality of the image. The optimization of the image extracts the characteristics of the image instead of restoring the degraded image. The improvement of the image involves the degraded image processing and the improvement of its visual aspect. A lot of research has been done to improve the image. Many research works have been done in this field. One among them is deep learning. Most of the existing contrast enhancement methods, adjust the tone curve to correct the contrast of an input image but doesn’t work efficiently due to limited amount of information contained in a single image. In this research, the CNN with edge adjustment is proposed. By applying CNN with Edge adjustment technique, the input low contrast images are capable to adapt according to high quality enhancement. The result analysis shows that the developed technique significantly advantages over existing methods.

scholarly journals Image Enhancement for Surveillance Video of Coal Mining Face Based on Single-Scale Retinex Algorithm Combined with Bilateral Filtering

Symmetry ◽  
2017 ◽  
Vol 9 (6) ◽  
pp. 93 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Rongxin Xu ◽  
Chao Tan ◽  
Xinhua Liu, ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Coal Mining ◽  
Bilateral Filtering ◽  
Surveillance Video ◽  
Single Scale ◽  
Retinex Algorithm ◽  
Mining Face

scholarly journals Low-Light Image Enhancement Based on Generative Adversarial Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Nandhini Abirami R. ◽  
Durai Raj Vincent P. M.
Keyword(s):  
Image Enhancement ◽  
Visual Information ◽  
Low Light ◽  
Generative Adversarial Network ◽  
Light Image ◽  
Adversarial Network ◽  
Benchmark Datasets ◽  
Dim Light ◽  
Effective Network

Image enhancement is considered to be one of the complex tasks in image processing. When the images are captured under dim light, the quality of the images degrades due to low visibility degenerating the vision-based algorithms’ performance that is built for very good quality images with better visibility. After the emergence of a deep neural network number of methods has been put forward to improve images captured under low light. But, the results shown by existing low-light enhancement methods are not satisfactory because of the lack of effective network structures. A low-light image enhancement technique (LIMET) with a fine-tuned conditional generative adversarial network is presented in this paper. The proposed approach employs two discriminators to acquire a semantic meaning that imposes the obtained results to be realistic and natural. Finally, the proposed approach is evaluated with benchmark datasets. The experimental results highlight that the presented approach attains state-of-the-performance when compared to existing methods. The models’ performance is assessed using Visual Information Fidelitysse, which assesses the generated image’s quality over the degraded input. VIF obtained for different datasets using the proposed approach are 0.709123 for LIME dataset, 0.849982 for DICM dataset, 0.619342 for MEF dataset.

Perbandingan Performa Histogram Equalization untuk Peningkatan Kualitas Gambar Minim Cahaya pada Android

2020 ◽  
Vol 12 (2) ◽  
pp. 80-88
Author(s):  
Claudia Kenyta ◽  
Daniel Martomanggolo Wonohadidjojo
Keyword(s):  
Image Enhancement ◽  
Light Condition ◽  
Histogram Equalization ◽  
Low Light ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Better Than

When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Equalization (CLAHE). This paper shows the implementation of HE algorithm and its performance in enhancing the quality of photos taken in low light condition on Android based application and the comparison with CLAHE algorithm. The results show that, HE algorithm is better than CLAHE algorithm.

scholarly journals Development and Implementation of AUV for Data Acquisition and Image Enhancement

2020 ◽  
Vol 9 (4) ◽  
pp. 2088-2093
Keyword(s):  
Light Intensity ◽  
Edge Detection ◽  
Image Enhancement ◽  
Visual Observation ◽  
Low Light ◽  
Canny Edge Detection ◽  
Underwater Environment ◽  
Web Camera ◽  
Canny Edge

In our proposed work, the developed Acoustic Unmanned Vehicle(AUV) moves in the direction of up/down. In general AUV is mainly used to for visual observation of the underwater environment by using a web camera. The acquired data from the AUV was preprocessed and the same image used for enhancement.. In this project, the image enhancement alogrithms has been implemented and the same has been computed for Canny Edge Detection, Hue, Luma and Saturation. From these computed results, we are able to enhance the quality of images for the conditions like low depth, low light intensity and color contrasting has been achieved using LABVIEW.

scholarly journals Low-Light Image Enhancement Base on Brightness Attention Mechanism Generative Adversarial Networks

2021 ◽  
Author(s):  
Chunzhi Wang ◽  
Zeyu Ma ◽  
Jiarun Fu ◽  
Rong Gao ◽  
Hefei Ling ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Social Life ◽  
Smart Cities ◽  
Attention Mechanism ◽  
Generative Adversarial Networks ◽  
Low Light ◽  
Data Set ◽  
Light Image ◽  
Adversarial Networks

Abstract With the development of intelligent technology, the concept of smart city, which is used to optimize urban management and services and improve the quality of life of citizens, has gradually been integrated into human social life. However, dark lighting environments in reality, such as insufficient light at night, cause or block photographic images in low brightness, severe noise, and a large number of details are lost, resulting in a huge loss of image content and information, which hinders further analysis and use. Such problems not only exist in the development of smart cities, but also exist in traditional criminal investigation, scientific photography and other fields, such as the accuracy of low-light image. However, in the current research results, there is no perfect means to deal with the above problems. Therefore, the study of low-light image enhancement has important theoretical significance and practical application value for the development of smart cities. In order to improve the quality of low-light enhanced images, this paper tries to introduce the luminance attention mechanism to improve the enhancement efficiency. The main contents of this paper are summarized as follows: using the attention mechanism, a method of low-light image enhancement based on the brightness attention mechanism to generative adversarial networks is proposed. This method uses brightness attention mechanism to predict the illumination distribution of low-light image and guides the enhancement network to enhance the image adaptiveness in different luminance regions. At the same time, u-NET network is designed and constructed to improve the modeling process of low-light image. We verified the performance of the algorithm on the synthetic data set and compared it with traditional image enhancement methods (HE, SRIE) and deep learning methods (DSLR). The experimental results show that our proposed network model has relatively good enhancement quality for low-light images, and improves the overall robustness, which has practical significance for solving the problem of low-light image enhancement in smart cities.

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