scholarly journals Noise Suppression and Contrast Enhancement via Bayesian Residual Transform (BRT) in Low-Light Conditions

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Audrey G. Chung ◽  
Alexander Wong
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Noise Suppression ◽  
State Of The Art ◽  
Light Conditions ◽  
Low Light ◽  
Preliminary Results ◽  
Denoised Image

Very low-light conditions are problematic for current robotic visionalgorithms as captured images are subject to high levels of ISOnoise. We propose a Bayesian Residual Transform (BRT) model forjoint noise suppression and image enhancement for images capturedunder these low-light conditions via a Bayesian-based multiscaleimage decomposition. The BRT models a given image as thesum of residual images, and the denoised image is reconstructedusing a weighted summation of these residual images. We evaluatethe efficacy of the proposed BRT model using the VIP-LowLightdataset, and preliminary results show a notable visual improvementover state-of-the-art denoising methods.

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 Contrast and Synthetic Multiexposure Fusion for Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Marwan Ali Albahar
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
State Of The Art ◽  
Input Image ◽  
Synthetic Image ◽  
Improve Image Quality ◽  
Exposure Fusion ◽  
Novel Technique ◽  
Lighting Conditions

Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.

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.

Contrast Enhancement for Low-light Image Enhancement: A Survey

2018 ◽  
Vol 7 (1) ◽  
pp. 36-48 ◽  
Author(s):  
Seonhee Park ◽  
Kiyeon Kim ◽  
Soohwan Yu ◽  
Joonki Paik
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Low Light ◽  
Light Image

Research on the Image Enhancement Algorithm of Pointer Instrument under Inadequate Light

2014 ◽  
Vol 615 ◽  
pp. 248-254 ◽  
Author(s):  
Lu Zhang ◽  
Jin Lin Zhang ◽  
Ting Rui ◽  
Yue Wang ◽  
Yan Nan Wang
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Traditional Method ◽  
Histogram Equalization ◽  
Light Conditions ◽  
Low Light ◽  
Homomorphic Filtering ◽  
The Impact ◽  
Image Details ◽  
Enhancement Algorithm

For image processing, the recognition of pointer instrument’s reading by computer vision highly depends on brightness. An image enhancement algorithm based on homomorphic filtering and histogram equalization is proposed in order to reduce the impact of low-light conditions on images of pointer instrument. It combines the methods of spatial with frequency domain, which enhances the image contrast and highlights the image details as well. Compared with the traditional method, the experiments show that the proposed method can eliminate the effect of inadequate light and also perform well in enhancing the image quality.

scholarly journals Recognition of vehicle make and model in low light conditions

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Aymen Fadhil Abbas ◽  
Usman Ullah Sheikh ◽  
Mohd Norzali Haji Mohd
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Operating Conditions ◽  
Light Conditions ◽  
Low Light ◽  
Lighting Condition ◽  
Front View ◽  
Recognition Model ◽  
Lighting Conditions ◽  
Model Recognition

This paper presents a method for vehicle make and model recognition (MMR) in low lighting conditions. While many MMR methods exist in the literature, these methods are designed to be used only in perfect operating conditions. The various camera configuration, lighting condition, and viewpoints cause variations in image quality.  In the presented method, the vehicle is first detected, image enhancement is then carried out on the detected front view of the vehicle, followed by features extraction and classification. The performance is then examined on a low-light dataset. The results show around 6% improvement in the ability of MMR with the use of image enhancement over the same recognition model without image enhancement.

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