Query: Low illumination

title: Low Illumination Image Enhancement Algorithm Based on a Physical Lighting Model
abstract: Low illumination images are usually taken in non uniform environmental light, such as extremely dark or bright light or artificial light. The enhancement results achieved by existing techniques are prone to halo artifacts, color unnaturalness, and information loss. To address these problems, we present a physical lighting model that describes the degradation of poor illumination images, in which the environmental light is a point wise variable and changes with the local light source. As long as the parameters in the model are properly estimated, the low illumination images can be directly recovered by solving the model. First, the initial environmental light can be considered as the incident component according to the Retinex theory and estimated via a Gaussian surrounding function. Second, the environmental light and light scattering attenuation rate are iteratively adjusted with the information loss constraint. Finally, to restrain the halo and block effects, the two parameters are refined by the weighted guide filter. The experimental results indicate that the proposed algorithm can improve the appearance of low illumination images that are captured in different scenes, reveal the details in textured regions with few halo effects, increase the richness of the visible edges, retain color consistency and reproduce the color quality and naturalness.

title: Smart scanning for low illumination and fast RESOLFT nanoscopy in vivo
abstract: RESOLFT fluorescence nanoscopy can nowadays image details far beyond the diffraction limit. However, signal to noise ratio (SNR) and temporal resolution are still a concern, especially deep inside living cells and organisms. In this work, we developed a non deterministic scanning approach based on a real time feedback system which speeds up the acquisition up to 6 fold and decreases the light dose by 70 90% for in vivo imaging. Also, we extended the information content of the images by acquiring the complete temporal evolution of the fluorescence generated by reversible switchable fluorescent proteins. This generates a series of images with different spatial resolution and SNR, from conventional to RESOLFT images, which combined through a multi image deconvolution algorithm further enhances the effective resolution. We reported nanoscale imaging of organelles up to 35 Hz and actin dynamics during an invasion process at a depth of 20 30 um inside a living Caenorhabditis elegans worm.Long acquisition time and high illumination intensities needed in super resolution imaging often generate photobleaching and phototoxicity. Here the authors develop a non deterministic scanning approach based on a real time feedback system that enables faster acquisition with lower light doses for in vivo imaging.

title: Low illumination underwater light field images reconstruction using deep convolutional neural networks
abstract: Underwater optical images are usually influenced by low lighting, high turbidity scattering and wavelength absorption. To solve these issues, a great deal of work has been performed to improve the quality of underwater images. Most of them use the high intensity LEDs for lighting to obtain the high contrast images. However, in high turbidity water, high intensity LEDs cause strong scattering and absorption. In this paper, we propose a light field imaging approach for solving underwater imaging problems in a low intensity light environment. As a solution, we tackle the problem of de scattering from light field images by using deep convolutional neural networks with depth estimation. Furthermore, a spectral characteristic based color correction method is used for recovering the color reduction. Experimental results show the effectiveness of the proposed method by challenging real world underwater imaging.

title: Adaptive image enhancement method for correcting low illumination images
abstract: In this study, to improve the adaptability of image enhancement in images with low illumination, a colored image correction method based on nonlinear functional transformation according to the illumination reflection model and multiscale theory is proposed. First, the original RGB image is converted to HSV color space, and the V component is used to extract the illumination component of the scene using the multiscale Gaussian function. Then, a correction function is constructed based on the Weber Fechner law, and two images are obtained through adaptive adjustments to the image enhancement function parameters based on the distribution profiles of the illumination components. Finally, an image fusion strategy is formulated and used to extract the details from the two images. Compared with the classic algorithm, the proposed algorithm can improve the overall brightness and contrast of an image while reducing the impact of uneven illumination. The enhanced images appear clear, bright, and natural.

title: Deep Learning based Effective Surveillance System for Low Illumination Environments
abstract: Surveillance cameras are installed in various locations and contribute to security maintenance and safety. Thus, the video quality of surveillance cameras is important for safety. However, in situations such as nighttime, low illumination often causes poor image quality. To solve this problem, we propose a system to help acquire quality images of general surveillance cameras utilized in various places through a combination of image quality improvement networks and object detect networks. This will improve safety in low illumination areas at night. It is also possible to establish a more effective monitoring system for situations occurring in low illumination areas.

title: Nighttime low illumination image enhancement with single image using bright/dark channel prior
abstract: Nighttime low illumination image enhancement is highly desired for outdoor computer vision applications. However, few works have been studied towards this goal. In addition, the low illumination enhancement problem becomes very challenging when the depth information of a low illumination image is unknown. To address this problem, in this paper, we propose a dual channel prior based method for nighttime low illumination image enhancement with a single image, which builds upon two existing image priors: dark channel prior and bright channel prior. We utilize the bright channel prior to get an initial transmission estimate and then use the dark channel as a complementary channel to correct potentially erroneous transmission estimates attained from the bright channel prior. Experimental results show significant credibility of the approach both visually and by quantitative comparison with existing methods.

title: Enhancement of Low Illumination Images based on an Optimal Hyperbolic Tangent Profile
abstract: Contrast enhancement is a critical pre processing stage for many image based applications. It is frequently encountered that the illumination condition, while capturing the image, is imperfect. Specific algorithms have to be applied to restore these images from, for instance, the degradation due to low illumination. An adaptive enhancement method is developed here that tackles the image quality enhancement problem from an optimization perspective. In particular, the input image intensity is mapped to the output based on a weighted hybrid of a hyperbolic tangent and a linear profile. The mapping parameters are optimized, with regard to maximizing the image global entropy, by using the Golden Section Search algorithm for its implementation efficiency. Moreover, user interventions are not necessary. Better qualitative and comparable quantitative performances are obtained from experiments, with regard to the increase of brightness, information content and suppression of unwanted artifacts, as compared to recent profile mapping based methods.

title: Long term Tai Chi exercise increases body stability of the elderly during stair ascent under high and low illumination*
abstract: The effects of long term Tai Chi exercise on body stability of the elderly during stair ascent under high and low illumination were investigated. Forty five healthy elderly women were divided into three groups, namely, Tai Chi exercise group, brisk walking group and no exercise control group. All the participants ascended a staircase, during which force platforms and a motion capture system collected the data. Under the high illumination, Tai Chi exercise participants exhibited higher loading rate and anteroposterior centre of pressure (COPap) displacement as well as a lower braking impulse than no exercise group. Under the low illumination, Tai Chi exercise participants demonstrated higher COPap and mediolateral centre of pressure (COPml) displacements as well as lower braking and lateral impulses compared with no exercise participants. The centre of mass (COM)ml sway in Tai Chi and no exercise participants were higher, the loading rates in Tai Chi and walking participants were higher, and the lateral impulse in no exercise participants was higher under low illumination than under high illumination. Thus, low illumination increases the risk of falling. Tai Chi participants increased their foot clearance, head inclination angle and COPap displacement under low illumination to increase their stability during stair ascent.

title: Low Illumination Color Image Enhancement Using Intuitionistic Fuzzy Sets
abstract: Because low illumination color image has the features of low brightness, poor contrast and dark color, and the enhancement effect of traditional image enhancement algorithm is very limited. A low illumination image enhancement algorithm based on fuzzy set theory is proposed, by transformed the RGB image into HSV space, and the brightness component V of the image is used to enhance the image in fuzzy plane. The experimental results show that this method is better than the traditional enhancement according to fuzzy set and the operation efficiency is higher, which can realize the clearness processing of low illumination image effectively.

title: Low illumination person re identification
abstract: Low illumination is a common problem for recognition and tracking. Low illumination video based person re identification (re id) is an important application in practice. Low illumination usually results in severe loss of visual appearance and space time information contained in pedestrian image or video, which brings large difficulty to re identification. However, the problem of low illumination video based person re id (LIVPR) has not been well studied. In this paper, we propose a novel triplet based manifold discriminative distance learning (TMD2L) approach for LIVPR. By regarding each video as an image set, TMD2L aims to learn a manifold based distance metric, under which the intrinsic structure of image sets can be preserved, and the distance between truly matching sets is smaller than that between wrong matching sets. Experiment results on the new collected low illumination person sequence (LIPS) dataset, as well as two simulated datasets LI PRID 2011 and LI iLIDS VID show that our proposed approach TMD2L outperforms existing representative person re id methods.