Global Contrast Research Articles

Blind quality index for tone-mapped images based on luminance partition

Tone-mapping operators (TMOs), which are designed to convert high dynamic range (HDR) images to standard low dynamic range (LDR) images for displaying on conventional devices, have gained extensive attention recently. The quality of tone-mapped images generated by different TMOs varies significantly, which depend upon the image contents and the parameter settings. A quality index that can accurately evaluate the performances of TMOs is thus highly needed. With this motivation, this paper presents a blind quality index based on luminance partition for tone-mapped images. It is based on the fact that the Human Visual System (HVS) has different sensitivities to image regions with different luminance levels. Specifically, two adaptive thresholds are first employed to segment an image into the dark, bright and normal areas. Then, we calculate the quality-aware features from different luminance areas: 1) local entropy feature is extracted from the dark and bright areas to measure the information loss due to the overexposure or underexposure during the tone mapping process; 2) local colorfulness feature is extracted from the normal area to evaluate the reproduction of colors. With the consideration that the perception of image quality depends on the combined effects of the salient local distortion and global quality degradation, the global contrast feature is also calculated and integrated for better evaluation performance. Moreover, to take advantage of the hierarchical characteristic of the HVS, all features are calculated under a multi-resolution framework. Eventually, the extracted features are mapped into an objective quality score based on the random forest regression. The proposed metric is shown to outperform those state-of-the-art metrics according to extensive experiments conducted on two publicly available databases.

Global and Adaptive Contrast Enhancement for Low Illumination Gray Images

Aiming at solving the problems of overall darkness, uneven illumination and low contrast of image under low illumination conditions, we present a global and adaptive contrast enhancement algorithm for low illumination gray images in this paper. The proposed algorithm is based on the Bilateral Gamma Adjustment function and combined with the particle swarm optimization (PSO). For the PSO, the gray standard variance is integrated into the evaluation function. To reconcile the dilemma of promoting the gray values of dark areas and suppressing the gray values of local bright areas at the same time, the information of entropy, edge content, and gray standard variance are used as the objective function for each particle to evaluate the gray image enhancement results. Then, the algorithm globally enhances the quality of the image by determining the optimal $\alpha $ value. Meanwhile, the learning factors of the PSO are updated during the iteration of optimization in the proposed algorithm. Compared with histogram equalization (HE), double plateau histogram equalization (DPHE), contrast limited adaptive histogram equalization (CLAHE), linear contrast stretching (LCS), adaptive gamma correction weighting distribution (AGCWD), the traditional PSO and MSF-PSO algorithm, the proposed algorithm significantly enhances the visual effect of the low illumination gray images. The experimental results demonstrate the superior capabilities of the proposed algorithm in enhancing the contrast of the image, such as improving the overall visual effect of the low illumination gray image and avoiding over-enhancement in the local area (s).

Modified Local and Global Contrast Enhancement Algorithm for Color Satellite Image

The quality of remotely sensed satellite images depends on the reflected electromagnetic radiation from the earth’s surface features. Lack of consistent and similar amounts of energy reflected by different features from the earth’s surface results in a poor contrast satellite image. Image enhancement is the image processing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present a detailed model for color image enhancement using the quaternion framework. We introduce a novel quaternionic frequency enhancement algorithm that can combine the color channels and the local and global image processing. The basic idea is to apply the α-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The parameter alfa for every block and the weights for every local and global enhanced image driven through optimization of measure of enhancement (EMEC). Some presented experimental results illustrate the performance of the proposed approach on color satellite images in comparison with the state-of-the-art methods.

Single Image Defogging Based on Multi-Channel Convolutional MSRCR

In order to solve the problem of image degradation in foggy weather, a single image defogging method based on a multi-scale retinex with color restoration (MSRCR) of multi-channel convolution (MC) is proposed. The whole defogging process mainly consists of four key parts: estimation of illumination components, guided filter operation, reconstruction of fog-free images, and white balance operation. First, the multi-scale Gaussian kernels are employed to extract precise features to estimate the illumination component. After that, the MSRCR method is applied to enhance the global contrast, detail information, and color restoration of the image. Second, the smoothing constraints of both illumination component and reflected component are considered together by using the guided filter twice, thus the enhanced image satisfies the smoothing constraint and the noise in the enhanced image is reduced. Third, the enhanced image by the MSRCR and the image processed by the secondary guided filter are fused by linear weighting to reconstruct the final fog-free image. Finally, in order to eliminate the influence of illumination on the color of the defogged image, the final defogged image is processed by white balance. The experimental results demonstrated that the proposed method can outperform state-of-the-art methods in both qualitative and quantitative comparisons.

Single Underwater Image Enhancement Based on $L_{P}$ -Norm Decomposition

Integration of ocean monitoring networks with artificial intelligence has become a popular topic for researchers. Artificial intelligence plays an important role in underwater image processing. For optical images captured in an underwater environment, the light scattering and absorption effect caused by the water medium results in poor visibility, such as blur and color casts. A novel approach is proposed herein to enhance the single underwater image with poor visibility. Similar to other image enhancement strategies built on fusion principles, our method also generates two input channels from the original degraded image, and these two channels are modulated by their corresponding weight measures. However, the main innovation of our method is that we propose a new multilevel decomposition approach based on I p -norm (ρ = 0, 1, 2) decomposition. According to the different sparse representation abilities of I p -norm to an image's spatial information, our approach decomposes the image into three levels: detail level, structure level, and illuminance level. Thus, these three levels can be manipulated separately. Because this new decomposition approach is based on image structural contents, rather than direct per-pixel downsampling that is utilized in traditional multi-resolution pyramid decomposition, it is more accurate and flexible. Additionally, according to specific underwater imaging conditions, we carefully select two input channels and their three associated global contrast, local contrast, and saliency weight measures. Our method generates output with more accurate details and a better illuminant dynamic range. Generally, we are the first to impose an I p -norm-based decomposition strategy on underwater image restoration and enhancement. Extensive qualitative and quantitative evaluations demonstrate that our strategy yields better results than state-of-the-art algorithms.

Distance-Oriented Cuckoo Search enabled optimal histogram for underwater image enhancement: a novel quality metric analysis

ABSTRACTThe major problem in underwater image processing involves light attenuation. In order to overcome such light incorporation and dispersion issues, this paper proposes a new underwater image enhancement model that includes certain phases like contrast correction and colour correction. Here, two processes are worked out in contrast correction namely (i) global contrast correction and (ii) local contrast correction. Furthermore, as the histogram evaluation is the major part of any image enhancement, it is very important to choose the optimal limits of the histogram. To solve this optimization problem, this paper introduces a new optimization model namely Distance Oriented Cuckoo Search (DOCS) algorithm that also satisfies the objective function defined in this paper. The major process of this model focusses on Integrated Global and Local Contrast Correction (IGLCC). Finally, the proposed model compares its performance over other methods.

Learning adaptive contrast combinations for visual saliency detection

Visual saliency detection plays a significant role in the fields of computer vision. In this paper, we introduce a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL) framework, which is able to adaptively combine different contrast measurements in a supervised manner. As most influential factor is contrast operation in bottom-up visual saliency, an average weighted corner-surround contrast (AWCSC) is first designed to measure local visual saliency. Combined with common-used center-surrounding contrast (CESC) and global contrast (GC), three types of contrast operations are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast feature maps, yielding more robust and accurate saliency maps. We evaluated our method for two main visual saliency detection tasks: human fixed eye prediction and salient object detection. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the integration is superior than individual subcomponent.

Development of a faster classification system for metal parts using machine vision under different lighting environments

A machine vision system for the automatic classification process is developed under different lighting environments, and has been applied to the operation of a robot arm with 6 degrees of freedom (DOF). In order to obtain accurate positioning information, the overall image is captured by a CMOS camera which is mounted above the working platform. The effects of back-lighting and front-lighting environments to the proposed system were investigated. With the front-lighting environment, four different conditions were performed. For each condition, global and local contrast threshold operations were used to obtain good image quality. In this study, a quadratic transformation used to describe the relationship between the image coordinates and the world coordinates was proposed, which has been compared to linear transformation as well as the camera calibration model in MATLAB tool. Experimental results show that in a back-lighting environment, the image quality is improved, such that the positions of the centers of objects are more accurate than in a front-lighting environment. According to the calibration results, the quadratic transformation is more accurate than other methods. By calculating the calibration deviation using the quadratic transformation, the maximum positive deviation is 0.48 mm and 0.38 mm in the X and Y directions, respectively. The maximum negative deviation is − 0.34 mm and − 0.43 mm in X and Y directions, respectively. The proposed system is effective, robust, and can be valuable to industry, as it offers an automated robotic system with an improved flexibility for separating dissimilar items of nominal value, in order to reclaim material investments, and decrease the expense of purchasing the same items again.

Global firing rate contrast enhancement in E/I neuronal networks by recurrent synchronized inhibition.

Inhibitory synchronization is commonly observed and may play some important functional roles in excitatory/inhibitory (E/I) neuronal networks. The firing rate contrast enhancement is a general feature of information processing in sensory pathways, and a new mechanism of contrast enhancement by inhibitory synchronization in E/I neuronal networks is investigated in this paper. Inspired by the firing rate contrast enhancement phenomenon by the lateral feed-forward inhibition, we reveal that the firing rate contrast enhancement could also occur by recurrent inhibition in E/I networks. It is further found that the synchronized inhibitory neurons act as a global inhibition which can enhance the firing rate contrast of excitatory neurons globally in synchronized E/I networks, even in partially synchronous states. Therefore, the firing rate contrast enhancement might be an important function of inhibitory synchronization and might facilitate information transmission in neural systems.

Contrast-enhanced fusion of infrared and visible images

The fusion of infrared and visible images may result in low contrast, which is unsuitable for observation by human eyes. Thus, we propose a contrast-enhanced fusion algorithm with nonsubsampled shearlet transform (NSST) frames, in which the NSST is first employed to decompose each of the source images into one low frequency sub-band and a series of high frequency sub-bands. To improve the fusion performance, we designed two measures for fusion of the low frequency and the high frequency: the low frequency is divided into salient and nonsalient regions in accordance with the human visual system to improve the global contrast by targeted fusion and the high frequency requires a local contrast fusion strategy. Finally, the merged sub-bands are constructed according to the selection principles, and the final fused image is produced by applying the inverse NSST on these merged sub-bands. Experimental results demonstrate the effectiveness and superiority of the proposed method over the state-of-the-art fusion methods in terms of both visual effect and objective evaluation results.

Adaptive Multi-Scale Entropy Fusion De-Hazing Based on Fractional Order

This paper describes a proposed fractional filter-based multi-scale underwater and hazy image enhancement algorithm. The proposed system combines a modified global contrast operator with fractional order-based multi-scale filters used to generate several images, which are fused based on entropy and standard deviation. The multi-scale-global enhancement technique enables fully adaptive and controlled color correction and contrast enhancement without over exposure of highlights when processing hazy and underwater images. This in addition to the illumination/reflectance estimation coupled with global and local contrast enhancement. The proposed algorithm is also compared with the most recent available state-of-the-art multi-scale fusion de-hazing algorithm. Experimental comparisons indicate that the proposed approach yields a better edge and contrast enhancement results without a halo effect, without color degradation, and is faster and more adaptive than all other algorithms from the literature.

Optical plasma boundary reconstruction based on least squares for EAST Tokamak

Reconstructing the shape and position of plasma is an important issue in Tokamaks. Equilibrium and fitting (EFIT) code is generally used for plasma boundary reconstruction in some Tokamaks. However, this magnetic method still has some inevitable disadvantages. In this paper, we present an optical plasma boundary reconstruction algorithm. This method uses EFIT reconstruction results as the standard to create the optimally optical reconstruction. Traditional edge detection methods cannot extract a clear plasma boundary for reconstruction. Based on global contrast, we propose an edge detection algorithm to extract the plasma boundary in the image plane. Illumination in this method is robust. The extracted boundary and the boundary reconstructed by EFIT are fitted by same-order polynomials and the transformation matrix exists. To acquire this matrix without camera calibration, the extracted plasma boundary is transformed from the image plane to the Tokamak poloidal plane by a mathematical model, which is optimally resolved by using least squares to minimize the error between the optically reconstructed result and the EFIT result. Once the transform matrix is acquired, we can optically reconstruct the plasma boundary with only an arbitrary image captured. The error between the method and EFIT is presented and the experimental results of different polynomial orders are discussed.

Unsupervised Saliency Detection in 3-D-Video Based on Multiscale Segmentation and Refinement

In this letter, we propose an unsupervised salient object detection method in three-dimensional videos. Both temporal and depth information are efficiently considered, and multiscale architecture and graph-based refinement are built to improve accuracy and robustness. First, the input video frame is segmented into nonoverlapping superpixels by combining both appearance and depth information at the input. A multiscale architecture is also deployed after the segmentation with different segmentation parameters. Second, the initial saliency score of each segmented superpixel in each scale is calculated via global contrast, which is defined by appearance, depth, and motion cues from two consecutive frames. Third, the initial saliency in each scale is refined by smoothing over graphs built by three spatial–temporal feature priors—color, depth, and motion. Finally, the result is obtained by fusing three refined saliency maps in three scales. The experiments on two widely used datasets illustrate that our method outperforms state-of-the-art algorithms in terms of accuracy, robustness, and reliability.

Relative efficacy of global motion versus contrast training early after stroke for recovering contrast sensitivity in cortical blindness

Relative efficacy of global motion versus contrast training early after stroke for recovering contrast sensitivity in cortical blindness

A Novel Contrast Enhancement Technique Based on Combination of Local and Global Statistical Data on Malaria Images

Malaria appears to be one of the main reasons for detrimental health issue at the global scale that is responsible for approximately half a million deaths every year. As the cases of malaria seem to escalate at an annual rate, it is vital to provide a rapid and accurate diagnosis through manual microscopic assessment in the attempt to control the spread of malaria. Nevertheless, varied staining steps and noise disruptions can cause inaccurate diagnosis due to wrong interpretation. Hence, to address such issues, this study investigated the performance upon removing background noise and the method of correcting illumination that has an impact upon segmentation for a computer-assisted diagnostic system. The findings display that the technique of based on Otsu threshold and statistic data used to enhance the contrast image as to determine cells infected by the malaria parasite, in comparison to other methods. In fact, this method was tested on 450 malaria images, which consisted of P. Vivax, P. Falciparum, and P. Knowlesi species at the stages of trophozoite, schizont, and gametocyte. As a result, the HSE approach yielded 1.31 for Global Contrast Factor (GCF), while 10.56 for Signal Noise Ratio (SNR).

Temporal adaptation control for local tone mapping operator

Abstract High dynamic range (HDR) imaging has gained great popularity over the past twenty years. Tone mapping operator (TMO) is the key component that enables reproduction of HDR images on the standard low dynamic range (LDR) display devices. When it comes to the HDR video, design of the TMO becomes especially challenging since temporal control of TMO parameters is needed in order to avoid possible artifacts. Since temporal and spatial contrast cannot be met simultaneously, existing solutions are usually designed to optimize one of these two requirements. We present novel local tone mapping operator that preserves details and simultaneously provides good local and global contrast of processed images. Tunable temporal control enables trade-off between spatial and temporal contrast of a tone mapped video. Flexible control presented in this paper ensures that both requirements can be met with a single operator just by using different tuning of the control block.When compared to the state-of-the-art TMOs, proposed solution exhibits better results regarding overall image quality.

Oretinex-DI: Pre-Processing Algorithms for Melanoma Image Enhancement

In Medical imaging, the dermoscopic images analysis is quite useful for the skin cancer detection. The automatic computer assisted diagnostic systems (CADS) require dermoscopic image enhancement for human perception and analysis. The traditional image enhancements methods lack the synchronization among contrast perception between human and the digital images. This paper proposes an optimized-Retinex (ORetinex) image enhancement algorithm to remove light effects, which is quite suitable for the dermoscopic image for clinical analysis for Melanoma. The value of global contrast factor (GCF) and contrast per pixel (CPP) is computed and compared with the traditional methods of image enhancements including contrast enhancement, CLAHE,Adaptive histogram equalization, Bilinear filtering and the proportion of GCF and CPP is found quite optimal as compare to these traditional methods.

A variational-based fusion model for non-uniform illumination image enhancement via contrast optimization and color correction

Abstract Non-uniform illumination images are of limited visibility due to under-exposure, over-exposure, or a combination of these two factors. Enhancing these images is a very challenging task in image processing. Although there are numerous enhancement methods to improve the visual quality of images, many of these methods produce undesirable results with regard to contrast and saturation improvements. In order to improve the visibility of images without over-enhancement or under-enhancement, a variational-based fusion method is proposed for adaptively enhancing non-uniform illumination images. First, a hue-preserving global contrast adaptive enhancement algorithm obtains a globally enhanced image. Second, a hue-preserving local contrast adaptive enhancement method produces a locally enhanced image. Finally, an enhanced result is obtained by a variational-based fusion model with contrast optimization and color correction. The final result represents a trade-off between global contrast and local contrast, and also maintains the color balance between the globally enhanced image and the locally enhanced image. This method produces visually desirable images in terms of contrast and saturation improvements. Experiments were conducted on a dataset that included different kinds of non-uniform illumination images. The results demonstrate that the proposed method outperforms the compared enhancement algorithms both qualitatively and quantitatively.

High dynamic range image tone mapping based on asymmetric model of retinal adaptation

Global tone mapping operators using the symmetrical retinal response model to light tend to produce a low dynamic range (LDR) image that exhibits loss of details of its corresponding high dynamic range (HDR) image in a bright or dark area. In this paper, we introduce a new asymmetric sigmoid curve (ASC) based on the model of retinal adaptation encompassing symmetrical S-shaped curve, and present two global tone mapping operators by using the ASC. In the proposed method, an ASC-based tone mapping function is obtained by using a well-known classic photography technique, called zone system. In addition, a contrast-enhancing tone mapping function is introduced by formulating a bi-criteria optimization problem with the luminance histogram of an input HDR image and the ASC-based mapping function. Experimental results demonstrate that the proposed method enhances the global contrast while preserving image details in the tone-mapped LDR image. Moreover, the objective assessment results using an image quality metric indicate that the proposed method shows a high performance to state-of-the-art global tone mapping operators.

A Deep Spatial Contextual Long-Term Recurrent Convolutional Network for Saliency Detection.

Traditional saliency models usually adopt hand-crafted image features and human-designed mechanisms to calculate local or global contrast. In this paper, we propose a novel computational saliency model, i.e., deep spatial contextual long-term recurrent convolutional network (DSCLRCN), to predict where people look in natural scenes. DSCLRCN first automatically learns saliency related local features on each image location in parallel. Then, in contrast with most other deep network based saliency models which infer saliency in local contexts, DSCLRCN can mimic the cortical lateral inhibition mechanisms in human visual system to incorporate global contexts to assess the saliency of each image location by leveraging the deep spatial long short-term memory (DSLSTM) model. Moreover, we also integrate scene context modulation in DSLSTM for saliency inference, leading to a novel deep spatial contextual LSTM (DSCLSTM) model. The whole network can be trained end-to-end and works efficiently when testing. Experimental results on two benchmark datasets show that DSCLRCN can achieve state-of-the-art performance on saliency detection. Furthermore, the proposed DSCLSTM model can significantly boost the saliency detection performance by incorporating both global spatial interconnections and scene context modulation, which may uncover novel inspirations for studies on them in computational saliency models.