Performance Of Computer Vision Algorithms Research Articles

A review on learning based image reflection removal algorithms

Capturing images through semi-reflective surfaces, such as glass windows and transparent enclosures, often leads to a reduction in visual quality and can adversely affect the performance of computer vision algorithms. As a result, image reflection removal has garnered significant attention among computer vision researchers. With the growing application of deep learning methods in various computer vision tasks, such as super-resolution, inpainting, and denoising, convolutional neural networks (CNNs) have become an increasingly popular choice for image reflection removal. The purpose of this paper is to provide a comprehensive review of learning-based algorithms designed for image reflection removal. Firstly, we provide an overview of the key terminology and essential background concepts in this field. Next, we examine various datasets and data synthesis methods to assist researchers in selecting the most suitable options for their specific needs and targets. We then review existing methods with qualitative and quantitative results, highlighting their contributions and significance in this field. Finally, some considerations about challenges and future scope in image reflection removal techniques are discussed.

Semantic Guided Single Image Reflection Removal

Reflection is common when we see through a glass window, which not only is a visual disturbance but also influences the performance of computer vision algorithms. Removing the reflection from a single image, however, is highly ill-posed since the color at each pixel needs to be separated into two values belonging to the clear background and the reflection, respectively. To solve this, existing methods use additional priors such as reflection layer smoothness, double reflection effect, and color consistency to distinguish the two layers. However, these low-level priors may not be consistently valid in real cases. In this paper, inspired by the fact that human beings can separate the two layers easily by recognizing the objects and understanding the scene, we propose to use the object semantic cue, which is high-level information, as the guidance to help reflection removal. Based on the data analysis, we develop a multi-task end-to-end deep learning method with a semantic guidance component, to solve reflection removal and semantic segmentation jointly. Extensive experiments on different datasets show significant performance gain when using high-level object-oriented information. We also demonstrate the application of our method to other computer vision tasks.

Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing

In recent decades, haze has become an environmental issue due to its effects on human health. It also reduces visibility and degrades the performance of computer vision algorithms in autonomous driving applications, which may jeopardize car driving safety. Therefore, it is extremely important to instantly remove the haze effect on an image. The purpose of this study is to leverage useful modules to achieve a lightweight and real-time image-dehazing model. Based on the U-Net architecture, this study integrates four modules, including an image pre-processing block, inception-like blocks, spatial pyramid pooling blocks, and attention gates. The original attention gate was revised to fit the field of image dehazing and consider different color spaces to retain the advantages of each color space. Furthermore, using an ablation study and a quantitative evaluation, the advantages of using these modules were illustrated. Through existing indoor and outdoor test datasets, the proposed method shows outstanding dehazing quality and an efficient execution time compared to other state-of-the-art methods. This study demonstrates that the proposed model can improve dehazing quality, keep the model lightweight, and obtain pleasing dehazing results. A comparison to existing methods using the RESIDE SOTS dataset revealed that the proposed model improves the SSIM and PSNR metrics by at least 5–10%.

Color Alignment for Relative Color Constancy via Non-Standard References.

Relative colour constancy is an essential requirement for many scientific imaging applications. However, most digital cameras differ in their image formations and native sensor output is usually inaccessible, e.g., in smartphone camera applications. This makes it hard to achieve consistent colour assessment across a range of devices, and that undermines the performance of computer vision algorithms. To resolve this issue, we propose a colour alignment model that considers the camera image formation as a black-box and formulates colour alignment as a three-step process: camera response calibration, response linearisation, and colour matching. The proposed model works with non-standard colour references, i.e., colour patches without knowing the true colour values, by utilising a novel balance-of-linear-distances feature. It is equivalent to determining the camera parameters through an unsupervised process. It also works with a minimum number of corresponding colour patches across the images to be colour aligned to deliver the applicable processing. Three challenging image datasets collected by multiple cameras under various illumination and exposure conditions, including one that imitates uncommon scenes such as scientific imaging, were used to evaluate the model. Performance benchmarks demonstrated that our model achieved superior performance compared to other popular and state-of-the-art methods.

LiCENt: Low-Light Image Enhancement Using the Light Channel of HSL

Images captured in low-brightness environments often lead to poor visibility and exhibit artifacts such as low brightness, low contrast, and color distortion. These artifacts not only affect the visual perception of the human eye but also decrease the performance of computer vision algorithms. Existing deep learning-based image enhancements studies are quite slow and usually require extensive hardware specifications. Conversely, lightweight enhancement approaches do not provide satisfactory performance as compared to state-of-the-art methods. Therefore, we proposed a fast and lightweight deep learning-based algorithm for performing low-light image enhancement using the light channel of Hue Saturation Lightness (HSL). LiCENt stands for Light Channel Enhancement Network that uses a combination of an autoencoder and convolutional neural network (CNN) to train a low-light enhancer to first improve the illumination and later improve the details of the low-light image in a unified framework. This method used a single channel lightness ‘L’ of HSL color space instead of traditional RGB color channels which helps in reducing the number of learnable parameters by a factor of 8.92, at the most. LiCENt also has significant advantages for the Brilliance Perception Adjustment, which enables the model to avoid issues including over-enhancement and color distortion. The experimental results demonstrate that our approach generalizes well in synthetic and natural low-light images and outperforms other methods in terms of qualitative and quantitative metrics.

Generation of the NIR Spectral Band for Satellite Images with Convolutional Neural Networks.

The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for landcover classification, especially concerning vegetation assessment. Despite the usefulness of NIR, it does not always accomplish common RGB. Modern achievements in image processing via deep neural networks make it possible to generate artificial spectral information, for example, to solve the image colorization problem. In this research, we aim to investigate whether this approach can produce not only visually similar images but also an artificial spectral band that can improve the performance of computer vision algorithms for solving remote sensing tasks. We study the use of a generative adversarial network (GAN) approach in the task of the NIR band generation using only RGB channels of high-resolution satellite imagery. We evaluate the impact of a generated channel on the model performance to solve the forest segmentation task. Our results show an increase in model accuracy when using generated NIR compared to the baseline model, which uses only RGB ( and F1-scores, respectively). The presented study shows the advantages of generating the extra band such as the opportunity to reduce the required amount of labeled data.

LighterGAN: An Illumination Enhancement Method for Urban UAV Imagery

In unmanned aerial vehicle based urban observation and monitoring, the performance of computer vision algorithms is inevitably limited by the low illumination and light pollution caused degradation, therefore, the application image enhancement is a considerable prerequisite for the performance of subsequent image processing algorithms. Therefore, we proposed a deep learning and generative adversarial network based model for UAV low illumination image enhancement, named LighterGAN. The design of LighterGAN refers to the CycleGAN model with two improvements—attention mechanism and semantic consistency loss—having been proposed to the original structure. Additionally, an unpaired dataset that was captured by urban UAV aerial photography has been used to train this unsupervised learning model. Furthermore, in order to explore the advantages of the improvements, both the performance in the illumination enhancement task and the generalization ability improvement of LighterGAN were proven in the comparative experiments combining subjective and objective evaluations. In the experiments with five cutting edge image enhancement algorithms, in the test set, LighterGAN achieved the best results in both visual perception and PIQE (perception based image quality evaluator, a MATLAB build-in function, the lower the score, the higher the image quality) score of enhanced images, scores were 4.91 and 11.75 respectively, better than EnlightenGAN the state-of-the-art. In the enhancement of low illumination sub-dataset Y (containing 2000 images), LighterGAN also achieved the lowest PIQE score of 12.37, 2.85 points lower than second place. Moreover, compared with the CycleGAN, the improvement of generalization ability was also demonstrated. In the test set generated images, LighterGAN was 6.66 percent higher than CycleGAN in subjective authenticity assessment and 3.84 lower in PIQE score, meanwhile, in the whole dataset generated images, the PIQE score of LighterGAN is 11.67, 4.86 lower than CycleGAN.

EAGNet: Elementwise Attentive Gating Network-Based Single Image De-Raining With Rain Simplification

Rain streaks are one of the main factors that degrade the performance of computer vision algorithms. Therefore, a preprocessing method is needed to remove rain streaks from rainy images. The main issue of the rain removal task is to prevent over (or under) de-raining. Over de-raining means that the background details are removed along with rain streaks in light rain, and under de-raining means that the rain streaks are not completely removed in heavy rain. These occur as the density of rain and intensity of rain streaks vary. In order to solve this, this paper proposes a two-step rain removal method. The proposed system first estimates the rain streaks image redefined with a simple operation from an input rainy image. The proposed rain streaks image contains rain density and rain streak intensity for the rainy image. By using this, the proposed system can adaptively remove rain streaks from images captured in various rain conditions. In addition, we propose a novel architectural unit, the elementwise attentive gating block, which is an optimized block used to deal with high frequency rain streaks. The proposed block selectively passes the desired components from the input feature maps by applying different weights to each element. It helps to clearly extract the rain streaks, and as a result, there are no traces of rain streaks on the restored image. The proposed method outperforms previous rain removal algorithms for both synthetic and real-world images.

A survey on generative adversarial networks for imbalance problems in computer vision tasks

Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images

The inherent imbalance in the data distribution of X-ray security images is one of the most challenging aspects of computer vision algorithms applied in this domain. Most of the prior studies in this field have ignored this aspect, limiting their application in the practical setting. This paper investigates the effect of employing Generative Adversarial Networks (GAN)-based image augmentation, or image synthesis, in improving the performance of computer vision algorithms on an imbalanced X-ray dataset. We used Deep Convolutional GAN (DCGAN) to generate new X-ray images of threat objects and Cycle-GAN to translate camera images of threat objects to X-ray images. We synthesized new X-ray security images by combining threat objects with background X-ray images, which are used to augment the dataset. Then, we trained various Faster (Region Based Convolutional Neural Network) R-CNN models using different augmentation approaches and evaluated their performance on a large-scale practical X-ray image dataset. Experiment results show that image synthesis is an effective approach to combating the imbalance problem by significantly reducing the false-positive rate (FPR) by up to 15.3%. The FPR is further improved by up to 19.9% by combining image synthesis and conventional image augmentation. Meanwhile, a relatively high true positive rate (TPR) of about 94% was maintained regardless of the augmentation method used.

MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15, along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16, which contains new challenging videos, and (iii) MOT17, that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes, but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light into potential future research directions.

A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography.

Architectural distortion (AD) is the earliest sign of breast cancer that can be detected on a mammogram, and it is usually associated with malignant tumors. Breast cancer is one of the major causes of death among women, and the chance of cure can increase significantly when detected early. Computer-aided detection (CAD) systems have been used in clinical practice to assist radiologists with the task of detecting breast lesions. However, due to the complexity and subtlety of AD, its detection is still a challenge, even with the assistance of CAD. Recently, the fusion of descriptors has become a trend for improving the performance of computer vision algorithms. In this work, we evaluated some local texture descriptors and their possible combinations, considering different fusion approaches, for application in CAD systems to improve AD detection. In addition, we present a novel fusion-based texture descriptor, the Completed Mean Local Mapped Pattern (CMLMP), which is based on complementary information between three LMP operators (signal, magnitude and center) and the local differences between pixel values and the mean value of a neighborhood. We compared the performance of the proposed descriptor with two other well-known descriptors: the Completed Local Binary Pattern (CLBP) and the Completed Local Mapped Pattern (CLMP), for the task of detecting AD in 350 digital mammography clinical images. The results showed that the descriptor proposed in this work outperforms the others, for both individual and fused approaches. Moreover, the choice of the fusion operator is crucial because it results in different detection performances.

Reliable Vision-Based Grasping Target Recognition for Upper Limb Prostheses.

Computer vision has shown promising potential in wearable robotics applications (e.g., human grasping target prediction and context understanding). However, in practice, the performance of computer vision algorithms is challenged by insufficient or biased training, observation noise, cluttered background, etc. By leveraging Bayesian deep learning (BDL), we have developed a novel, reliable vision-based framework to assist upper limb prosthesis grasping during arm reaching. This framework can measure different types of uncertainties from the model and data for grasping target recognition in realistic and challenging scenarios. A probability calibration network was developed to fuse the uncertainty measures into one calibrated probability for online decision making. We formulated the problem as the prediction of grasping target while arm reaching. Specifically, we developed a 3-D simulation platform to simulate and analyze the performance of vision algorithms under several common challenging scenarios in practice. In addition, we integrated our approach into a shared control framework of a prosthetic arm and demonstrated its potential at assisting human participants with fluent target reaching and grasping tasks.

Fixed Pattern Noise Analysis for Feature Descriptors in CMOS APS Images

This paper provides a comparative performance evaluation of local features for images from CMOS APS sensors affected by fixed pattern noise for different combinations of common detectors and descriptors. Although numerous studies report comparisons of local features designed for ordinary visual images, their performance on images with fixed pattern noise is far less assessed. The goal of this work is to develop a tool that allows to evaluate the performance of computer vision algorithms and their implementations subject to deviations of the physical parameters of the CMOS sensor. This tool will facilitate the quantification of the high-level effects produced by circuit random noise, enabling the optimization of the sensor during the design flow with specifications much closer to the application scope. Likewise, this tool will provide the electronic designer with a relationship between high-level algorithm accuracy and maximum fixed pattern noise. Thus the contribution is double: (1) to evaluate the performance of both local float type and more recent binary type detectors and descriptors when combined under a variety of image transformations, and (2) to extract relevant information from circuit-level simulation and to develop a basic noise model to be employed in the design of the feature descriptor evaluation. The utility of this approach is illustrated by the evaluation of the effect of column-wise and pixel-wise fixed pattern noise at the sensor on the performance of different local feature descriptors.

Toward Specular Removal from Natural Images Based on Statistical Reflection Models

Removing specular reflections from images is critical for improving the performance of computer vision algorithms. Recently, state-of-the-art methods have demonstrated remarkably good performance at removing specular reflections from chromatic images. These methods are typically based on the chromatic pixels assumption; therefore, they are prone to failure in the achromatic regions. This paper presents a novel method that is applicable to natural images, because it is effective for both chromatic and achromatic regions. The proposed method is based on modeling the general properties of diffuse and specular reflections in a solid convex optimization framework. Considering the physical constraints, we determine the global optimal solution using the split Bregman method. Experimental results demonstrate the effectiveness of the proposed method, particularly for the achromatic regions, and its competence as a state-of-the-art method for removing specular reflections from the chromatic regions.

Robust image retrieval by cascading a deep quality assessment network

The performance of computer vision algorithms can severely degrade in the presence of a variety of distortions. While image enhancement algorithms have evolved to optimize image quality as measured according to human visual perception, their relevance in maximizing the success of computer vision algorithms operating on the enhanced image has been much less investigated. We consider the problem of image enhancement to combat Gaussian noise and low resolution with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising or super resolution, allowing for optimization of the enhancement CNN to maximize retrieval performance. This framework allows us to couple enhancement to the retrieval problem. We also consider the problem of adapting image features for robust retrieval performance in the presence of distortions. We show through experiments on distorted images of the Oxford and Paris buildings datasets that our algorithms yield improved mean average precision when compared to using enhancement methods that are oblivious to the task of image retrieval. 11The material in this paper appeared in part at the 2018 IEEE International Conference on Image Processing, Athens, Greece.

Low-Light Image Enhancement via the Absorption Light Scattering Model.

Low light often leads to poor image visibility, which can easily affect the performance of computer vision algorithms. First, this paper proposes the absorption light scattering model (ALSM), which can be used to reasonably explain the absorbed light imaging process for low-light images. In addition, the absorbing light scattering image obtained via ALSM under a sufficient and uniform illumination can reproduce hidden outlines and details from the low-light image. Then, we identify that the minimum channel of ALSM obtained above exhibits high local similarity. This similarity can be constrained by superpixels, which effectively prevent the use of gradient operations at the edges so that the noise is not amplified quickly during enhancement. Finally, by analyzing the monotonicity between the scene reflection and the atmospheric light or transmittance in ALSM, a new low-light image enhancement method is identified. We replace atmospheric light with inverted atmospheric light to reduce the contribution of atmospheric light in the imaging results. Moreover, a soft jointed mean-standard-deviation (MSD) mechanism is proposed that directly acts on the patches represented by the superpixels. The MSD can obtain a smaller transmittance than that obtained by the minimum strategy, and it can be automatically adjusted according to the information of the image. The experiments on challenging low-light images are conducted to reveal the advantages of our method compared with other powerful techniques.

Light Invariant Video Imaging for Improved Performance of Convolution Neural Networks

Light conditions affect the performance of computer vision algorithms by creating spatial changes in color and intensity across a scene. Convolutional neural networks (CNNs) use color components of the input image and, as a result, are sensitive to ambient light conditions. This work analyzes the influence of ambient light conditions on CNN classifiers. We suggest a method for boosting the performance of CNN-based object detection and classification algorithms by using light invariant video imaging (LIVI). LIVI neutralizes the influence of ambient light conditions and renders the perceived object’s appearance independent of the light conditions. Training sets consist mainly, if not only, of objects in natural light conditions. As such, using LIVI boosts CNN performance by matching object appearance to that expected by the CNN model, which was created according to the training set. We further investigate the use of LIVI as a general self-supervised learning framework for CNN. Faster region-based CNN (Faster R-CNN) was used as a case study in order to validate the importance of light conditions on CNN performance and on how it can be improved by using LIVI as an input or feedback mechanism in a self-supervised framework. We show that LIVI enables reduced CNN size, enhanced performance and improved training.

안개 제거 기술의 정량적인 성능 평가 기법 조사

Image dehazing has been extensively studied, but the performance evaluation method for dehazing techniques has not attracted significant interest. This paper surveys many existing performance evaluation methods of image dehazing. In order to analyze the reliability of the evaluation methods, synthetic hazy images are first reconstructed using the ground-truth color and depth image pairs, and the dehazed images are then compared with the original haze-free images. Meanwhile we also evaluate dehazing algorithms not by the dehazed images' quality but by the performance of computer vision algorithms before/after applying image dehazing. All the aforementioned evaluation methods are analyzed and compared, and research direction for improving the existing methods is discussed.

Evaluation of video activity localizations integrating quality and quantity measurements

Evaluating the performance of computer vision algorithms is classically done by reporting classification error or accuracy, if the problem at hand is the classification of an object in an image, the recognition of an activity in a video or the categorization and labeling of the image or video. If in addition the detection of an item in an image or a video, and/or its localization are required, frequently used metrics are Recall and Precision, as well as ROC curves. These metrics give quantitative performance values which are easy to understand and to interpret even by non-experts. However, an inherent problem is the dependency of quantitative performance measures on the quality constraints that we need impose on the detection algorithm. In particular, an important quality parameter of these measures is the spatial or spatio-temporal overlap between a ground-truth item and a detected item, and this needs to be taken into account when interpreting the results.We propose a new performance metric addressing and unifying the qualitative and quantitative aspects of the performance measures. The performance of a detection and recognition algorithm is illustrated intuitively by performance graphs which present quantitative performance values, like Recall, Precision and F-Score, depending on quality constraints of the detection. In order to compare the performance of different computer vision algorithms, a representative single performance measure is computed from the graphs, by integrating out all quality parameters. The evaluation method can be applied to different types of activity detection and recognition algorithms. The performance metric has been tested on several activity recognition algorithms participating in the ICPR 2012 HARL competition.