Gamma Transformation Research Articles

Supervised Learning for Predictive Pore Size Classification of Regenerated Cellulose Membranes Based on Atomic Force Microscopy Measurements.

Nanoporous dialysis membranes made of regenerated cellulose are used as molecular weight cutoff standards in bioseparations. In this study, mesoporous standards with Stokes’ radii (50 kDa/2.7 nm, 100 kDa/3.4 nm and 1000 kDa/7.3 nm) and overlapping skewed distributions were characterized using AFM, with the specific aim of generating pore size classifiers for biomimetic membranes using supervised learning. Gamma transformation was used prior to conducting discriminant analysis in terms of the area under the receiver operating curve (AUC) and classification accuracy (Acc). Monte Carlo simulations were run to generate datasets (n = 10) on which logistic regression was conducted using a constant ratio of 80:20 (measurement:algorithm training), followed by algorithm validation by WEKA. The proposed algorithm can classify the 1000 kDa vs. 100 kDa (AUC > 0.8) correctly, but discrimination is weak for the 100 kDa vs. 50 kDa (AUC < 0.7), the latter being attributed to the instrument accuracy errors below 5 nm. As indicated by the results of the cross-validation study, a test size equivalent to 70% (AUCtapping = 0.8341 ± 0.0519, Acctapping = 76.8% ± 5.9%) and 80% (AUCfluid = 0.7614 ± 0.0314, Acctfluid = 76.2% ± 1.0%) of the training sets for the tapping and fluid modes are needed for correct classification, resulting in predicted reduction of scan times.

Infrared scene prediction of night unmanned vehicles based on multi-scale feature maps

Recent advances achieve good performance in scene prediction, however, most approaches generate blurry and short-term predictions due to pixel-level prediction. Meanwhile, there is hardly any research on infrared scene prediction of night unmanned vehicles. In this paper, we propose a Multi-Scale Feature Map Network (MSFMNet) for infrared scene prediction of night unmanned vehicles to achieve real-time, realistic future predictions and long-term predictions. First, Contrast-Limited Adaptive Histogram Equalization and gamma transformation are used to preprocess infrared images, which improves the ability of network to extract more useful feature information. Second, an Atrous Spatial Pyramid network is designed to extract spatial features of small objects and contours, which benefits the modeling of detailed structure during decoding. Finally, a Multi-Scale Feature Maps Loss is proposed to supervise and guide the network, which achieves multi-frame infrared scene predictions. Extensive experiments on DHUdataset demonstrate the effectiveness of the proposed model with the state-of-the-art approaches, and at the same time, the model meets the real-time requirements and can predict future images after 1.2 s.

Virtual generation of pavement crack images based on improved deep convolutional generative adversarial network

To solve the problems associated with a small sample size during intelligent road detection, a virtual image set generation method for asphalt pavement cracks is proposed based on improved deep convolutional generative adversarial networks (DCGANs). First, a small set of sample crack images is collected and used as the basic image set to perform filtering, gamma transformation, and other processes, whereby crack feature recognition is enhanced. Second, a variational autoencoder (VAE) is used to encode real crack images. The latent variable values obtained from the VAE are provided as input to the DCGAN model generator, and the model hyperparameters are optimized. Subsequently, the adaptive moment estimation (Adam) optimizer is used to reoptimize the model and thereby improve the model convergence speed and generalization ability. The proposed method has the advantages of both VAE and DCGAN. Finally, a pavement crack classification detection model based on faster region convolutional neural network (Faster R-CNN) is used to evaluate the reliability of the generated crack images. The results show that the augmented dataset of the proposed method with the detection model has an average precision of 90.32%, which is higher than that of the conventional method evaluated using the same test dataset. The proposed method generates virtual crack images that are moderately identical to real ones, thereby solving the problem of insufficient image datasets of cracks in specific road sections. The method also provides data assurance for the intelligentization of pavement crack detection and the reduction of pavement maintenance costs.

Some questions of theory of functional Gamma transformation

Some questions of theory of functional Gamma transformation.

An Adaptive Recovering Algorithm for The Color Bar Code Image Based on Gamma Transforms

Aiming at the problem of error reading caused by color deviation under abnormal light source, a color bar code image restoration algorithm adaptively acquiring correction parameters is proposed. According to degree of exposure, the variation range of parameter γ is selected the step size is set. Gamma transformation is then implemented sequentially for the luminance component. Furthermore, the root means square error of enhanced image and source image, as well as the information entropy of enhanced image are sequentially obtained. The best balance point is captured, yielding corresponding γ for image recovery. Finally, the image is segmented based on histogram equalization principle to achieve the best recovery performance. The experimental results show that the algorithm is able to obtain the correction parameters adaptively and recover true color of color bar code images effectively under low-light or non-uniformly intense light of particular light sources.

Fast and Efficient Visibility Restoration Technique for Single Image Dehazing and Defogging

Poor weather conditions are detrimental to transportation systems and increase the likelihood of road accidents. Haze and fog are the most responsible atmospheric parameters affecting visibility and hence the traffic performance. The intelligent driving assistance systems developed for automatic vehicles use clear vision for various smart applications like keeping within the correct lane and recognize traffic signs. Bad weather decreases the visibility significantly based on the intensity of fog and haze. So, there is a need for the restoration of clear visibility. This paper introduces a novel visibility restoration approach based on thresholding and gamma transformation method. A step-by-step process for the proposed method is as follows. First, the proper selection of an atmospheric light value is responsible for the validation of the color and contrast of the recovered images. The threshold process makes it possible to estimate atmospheric light. Then, accurately estimating the transmission depth from object to object in small time is the most challenging aspect due to unequal distribution. To solve the problem, the gamma transformation method is used to correctly estimate the depth. Finally, restore the scene radiance from the hazy and foggy images. The experimental results show that the proposed measurement ensures good uniformity about qualitative and quantitative evaluation using eight performance metrics: contrast gain, percentage of saturated pixels, blind contrast assessment, structural similarity index measure (SSIM), image visibility measurement (IVM), mean square error (MSE), visual contrast measure (VCM), and peak signal-to-noise ratio (PSNR). The proposed algorithm excels in previous works in terms of processing time and visibility restoration results comparable to sophisticated state-of-the-art techniques.

Weak‐Light Image Enhancement Method Based on Adaptive Local Gamma Transform and Color Compensation

In weak‐light environments, images suffer from low contrast and the loss of details. Traditional image enhancement models are usually failure to avoid the issue of overenhancement. In this paper, a simple and novel correction method is proposed based on an adaptive local gamma transformation and color compensation, which is inspired by the illumination reflection model. Our proposed method converts the source image into YUV color space, and the Y component is estimated with a fast guided filter. The local gamma transform function is used to improve the brightness of the image by adaptively adjusting the parameters. Finally, the dynamic range of the image is optimized by a color compensation mechanism and a linear stretching strategy. By comparing with the state‐of‐the‐art algorithms, it is demonstrated that the proposed method adaptively reduces the influence of uneven illumination to avoid overenhancement and improve the visual effect of low‐light images.

Underwater image enhancement algorithm based on Retinex and wavelet fusion

Aiming at the problems of color cast, low contrast and fuzz in the original underwater image, this paper proposes an underwater image enhancement algorithm based on Retinex and wavelet fusion. First, this paper uses the multi-scale Retinex algorithm to perform color correction processing on underwater images. Then, this paper performs gamma transformation and sharpening on the image to improve the contrast and sharpness of the image. Finally, this paper uses wavelet fusion algorithm for image fusion to obtain underwater images with clear textures and details. Through subjective and objective evaluation with other algorithms, the algorithm proposed in this paper improves the color cast and fuzz, and improves the contrast and clarity of the image.

Adaptive power-law and cdf based geometric transformation for low contrast image enhancement

Image enhancement is a technique that manipulates an image to make it more meaningful and effective to user specific problem. In most of the enhancement techniques, input image intensities are transformed into either higher order or lower order intensities according to the designed algorithmic characteristic. But, in certain cases the input intensities might require to be transformed in a balanced combination of both higher and lower order intensity. Moreover, 2D Geometric Transformation is mainly used to transform the objects presents in an image. Here a contemplative fusion of gamma and 2D Geometric Transformation concept has been used for intensity transformation. The proposed method first divides the histogram into three sub-sections according to the homogeneity value representing the dark, gray and bright section of histogram. Then each sub-section is transformed locally using adaptive gamma and 2D Geometric scaling transformation. These transformed sub-sections are merged again by employing 2D translation operation. On the other hand, a global gamma transformation is obtained for entire histogram. At last, the final transformation matrix is obtained by combining previously computed local and global transformation. The comparison of this technique with other state of art technique has been discussed to depict the significance of the proposed method. The proposed method gives a new and innovative dimension of image enhancement.

A Light Adaptive Heart Rate Detection Method Based on Webcam

In order to extract the pulse wave signal of blood volume effectively in the case of uneven light, a light-adaptive heart rate detection method based on webcam was proposed. In this method, adaptive gamma transform is applied to face image sequence to eliminate the influence of illumination. The pulse wave source signal was extracted from the forehead area and the blood volume pulse wave was obtained by wavelet filtering. The heart rate is estimated by Fourier transform analysis. The Bland-Altman analysis indicates that the method used in this paper is in good agreement with the measurement results of the electronic sphygmomanometer, and the adaptive gamma transformation used in this paper eliminates the influence of light interference, and the measurement error of heart rate is significantly reduced, which is completely able to meet the requirements of daily heart rate monitoring.

Infrared Image Adaptive Enhancement Guided by Energy of Gradient Transformation and Multiscale Image Fusion

The detail enhancement and dynamic range compression of infrared (IR) images is an important issue and a necessary practical application in the domain of IR image processing. This paper provides a novel approach to displaying high dynamic range infrared images on common display equipment with appropriate contrast and clear detail information. The steps are chiefly as follows. First, in order to protect the weak global details in different regions of the image, we adjust the original normalized image into multiple brightness levels by adaptive Gamma transformation. Second, each brightness image is decomposed into a base layer and several detail layers by the multiscale guided filter. Details in each image are enhanced separately. Third, to obtain the image with global details of the input image, enhanced images in each brightness are fused together. Last, we filter out the outliers and adjust the dynamic range before outputting the image. Compared with other conventional or cutting-edge methods, the experimental results demonstrate that the proposed approach is effective and robust in dynamic range compression and detail information enhancement of IR image.

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC.

Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.

An Improved Infrared Image Contrast Enhancement Method

Aiming at the characteristics of infrared image, such as concentrated gray level distribution, low contrast and poor resolution, the paper proposes an improved infrared image enhancement algorithm. Firstly, the original image is filtered by adaptive median filtering method, then platform histogram equalization and gamma transformation are performed on the filtered image to improve the overall contrast of the image. At the same time, the filtered image is performed by Laplacian sharpening to get the edge information of the image. Finally, we obtained an enhanced infrared image by weighted combination of gamma transformation and Laplacian sharpening. Experiment shows that target contrast and average brightness are enhanced by the proposed algorithm, furthermore, the overall information is preserved and the edge information of the image is obtained, which improves the visual effect.

A novel framework for MR image segmentation and quantification by using MedGA

Background and Objectives: Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework tailored to effectively assist the physicians during their decision-making tasks.Methods: In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. Our framework exploits MedGA as a pre-processing stage. MedGA is an image enhancement method based on Genetic Algorithms that improves the threshold selection, obtained by the efficient Iterative Optimal Threshold Selection algorithm, between the underlying sub-distributions in a nearly bimodal histogram.Results: The results achieved by the proposed evolutionary framework were quantitatively evaluated, showing that the use of MedGA as a pre-processing stage outperforms the conventional image enhancement methods (i.e., histogram equalization, bi-histogram equalization, Gamma transformation, and sigmoid transformation), in terms of both MR image enhancement and segmentation evaluation metrics.Conclusions: Thanks to this framework, MR image segmentation accuracy is considerably increased, allowing for measurement repeatability in clinical workflows. The proposed computational solution could be well-suited for other clinical contexts requiring MR image analysis and segmentation, aiming at providing useful insights for differential diagnosis and prognosis.

Adaptive Weber-face for robust illumination face recognition

In this paper, an adaptive Weber-face method is presented to deal with varying lighting and extract illumination-insensitive representation for face recognition. A novel pixel-wise adaptive gamma transformation, which has been proved mathematically, is proposed to achieve a local uniform-illumination assumption in Weber-face. Next, the local binary patterns are extracted from the Weber-face images, and then further alleviate the effect of varying illumination. Experiments demonstrate that the proposed method achieves the high recognition rates of 99.55% and 96.63% on extended Yale B and CMU-PIE databases respectively, which outperforms the state-of-the-art methods.

On the Effect of γ-phase transformation kinetics upon microstructure response of Cold Heading Quality Steel

Cold heading quality CHQ steel is a versatile form over other steels as they are used non-heat treated; their strengthening mechanism is achieved through cold heading operations. Metal is therefore stretched without applying any source of heat, metal flow during the cold heading operation must depend on grains flow which increases the mechanical properties such as strength, resistance to indentation and toughness. It is therefore necessary to form the isotropic grains before applying cold heading operation may increase the properties. Gamma phase formation during the heat treatment is crucial factor for cold operation. An effort is made in this research work to study and find out the Austenite nucleation and growth morphology of commercial CHQ steel through continuous heating experiments by utilizing lead-bath up-quenching technique at differentaustenizing temperature ranges. High class Optical Microscope Olympus GX51, scanning electron microscopy techniques have been utilized to reveal and interpret the microstructure and it was found that At the 740°C, the microstructure shows the lack of homogeneity in the structure hence cold-head-ability of CHQ steel is anisotropic but at the high temperature in austenite domain at 60sec the resultant austenite is highly homogenous due to high volume fraction of austenite has been formed then the cold-head-ability properties of CHQ steel turned to be isotropic. Keywords: Up-quenching, cold heading steel, gamma transformation, heating rate etc.

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solution for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements.

Blind forensics of image gamma transformation and its application in splicing detection

Image forensics technology based on image manipulation detection has aroused great interest of researchers in recent decades. By revealing the traces of image manipulations, it could contribute to the location of tampered areas. This paper addresses the detection of image gamma transformation and its application in image splicing detection. A 5-dimensional feature vector is constructed based on the effects of gamma transformation on the histogram, and then Support Vector Machine (SVM) is trained to detect gamma transformation. For splicing tampered area locating, the investigated image is divided into overlapping blocks based on the sliding window, and then each pixel gets a probability of being gamma transformed according to the detection results in image blocks, based on which the tampered area is located. The experimental results validate the effectiveness of the proposed method when no post-operations are applied. Meanwhile, the proposed method is robust to the attack of rank filtering.

Flame : A flexible data reduction pipeline for near-infrared and optical spectroscopy

We present flame, a pipeline for reducing spectroscopic observations obtained with multi-slit near-infrared and optical instruments. Because of its flexible design, flame can be easily applied to data obtained with a wide variety of spectrographs. The flexibility is due to a modular architecture, which allows changes and customizations to the pipeline, and relegates the instrument-specific parts to a single module. At the core of the data reduction is the transformation from observed pixel coordinates (x, y) to rectified coordinates (lambda, gamma). This transformation consists in the polynomial functions lambda(x,y) and gamma(x,y) that are derived from arc or sky emission lines and slit edge tracing, respectively. The use of 2D transformations allows one to wavelength calibrate and rectify the data using just one interpolation step. Furthermore, the gamma(x,y) transformation includes also the spatial misalignment between frames, which can be measured from a reference star observed simultaneously with the science targets. The misalignment can then be fully corrected during the rectification, without having to further resample the data. Sky subtraction can be performed via nodding and/or modeling of the sky spectrum; the combination of the two methods typically yields the best results. We illustrate the pipeline by showing examples of data reduction for a near-infrared instrument (LUCI at the Large Binocular Telescope) and an optical one (LRIS at the Keck telescope).

Parameter estimation of image gamma transformation based on zero-value histogram bin locations

Gamma transformation plays an important role in image processing and image display. However, images may look either bleached out or too dark if transformed with improper parameters. While the value of gamma transformation parameter with which an image was created is not included in the current image standards, it is hard to restore the original image vision without the knowledge of gamma transformation. In this paper, a gamma transformation parameter estimation method is proposed based on Zero-Value Histogram Bin (ZVHB) locations. The method firstly exploits the relationship between the number of ZVHBs and the parameter of gamma transformation to get an approximate value of the parameter and an interval to which the parameter belongs. Then in the interval, the parameter is searched from the approximate value by matching the ZVHB locations of the investigated image and the reference images transformed with two close parameters. The experimental results validate the effectiveness of the proposed method, which outperforms the state-of-the-art methods. Meanwhile, the improperly transformed images can be restored visually by applying inverse gamma transformation with the estimated parameters.