Contrast-Aware Color Consistency Correction for Multiple Images

The global optimization-based color correction approach aims to minimize the color differences of multiple images by optimizing the correction model for each image. The color differences in multisource and multitemporal remote sensing images are difficult to express using a simple correction model with few parameters. When employing a more flexible correction model, the number of correction parameters and optimization equations grows rapidly with the increase in the number and resolution of input images. In addition, the correction parameters of all images are coupled together and need to be solved simultaneously. An excessive number of parameters results in solving slowly or potential failure. To solve this problem, we propose a parallelizable color correction approach that decouples the correlation of correction parameters in the optimization equations and optimizes each image separately. First, we introduce auxiliary variables that replace values related to other images in the cost function. Second, we construct optimization equations for each image and parallelly solve the correction parameters. Finally, we correct the input images through a weighted correction model to better eliminate correction artifacts. Our approach iteratively optimizes auxiliary variables and correction parameters until the correction results converge. The experimental results on several challenging datasets show that our approach significantly improves execution efficiency and obtains the global optimal solution using the flexible correction model.

Hybrid enhanced whale optimization algorithm for contrast and detail enhancement of color images

Contrast-enhanced (CE) breast imaging involves the injection contrast agents (i.e., iodine) to increase conspicuity of malignant lesions. CE imaging may be used in conjunction with digital mammography (DM) or digital breast tomosynthesis (DBT) and has shown promise in improving diagnostic specificity. Both CE-DM and CE-DBT techniques require optimization as clinical diagnostic tools. Physical factors including x-ray spectra, subtraction technique, and the signal from iodine contrast, must be considered to provide the greatest object detectability and image quality. We developed a cascaded linear system model (CLSM) for the optimization of CE-DM and CE-DBT employing dual energy (DE) subtraction or temporal (TE) subtraction. We have previously developed a CLSM for DBT implemented with an a-Se flat panel imager (FPI) and filtered backprojection (FBP) reconstruction algorithm. The model is used to track image quality metrics - modulation transfer function (MTF) and noise power spectrum (NPS) - at each stage of the imaging chain. In this study, the CLSM is extended for CE breast imaging. The effect of x-ray spectrum (varied by changing tube potential and the filter) and DE and TE subtraction techniques on breast structural noise was measured was studied and included as a deterministic source of noise in the CLSM. From the two-dimensional (2D) and three-dimensional (3D) MTF and NPS, the ideal observer signal-to-noise ratio (SNR), also known as the detectability index (d'), may be calculated. Using d' as a FOM, we discuss the optimization of CE imaging for the task of iodinated contrast object detection within structured backgrounds. Increasing x-ray energy was determined to decrease the magnitude of structural noise and not its correlation. By performing DE subtraction, the magnitude of the structural noise was further reduced at the expense of increased stochastic (quantum and electronic) noise. TE subtraction exhibited essentially no residual structural noise at the expense of increased quantum noise, even over that of the DE case. For DE subtraction, optimization of dose weighting to the HE view (fh ) results in the minimization of quantum noise. Both subtraction weighting factor (wSub ) and the iodine contrast signal were dependent on the LE and HE x-ray spectra. To best detect a 5 mm Gaussian lesion with 5 mg/ml of iodine within a 4 cm thick breast, it was found that the high energy (HE) view should be acquired with a tube potential of 47 kVp (W/Ti spectrum) and the low energy (LE) view with a potential of 23 kVp (W/Rh spectrum). Due to the complete removal of structural noise, TE subtraction produced much higher d' than DE subtraction both as a function of mean glandular dose and iodine concentration. We have shown the effect of increasing x-ray energy as well as projection domain subtraction on breast structural noise. Further, we have exhibited the utility of the CLSM for DE and TE subtraction CE imaging in the optimization of imaging parameters such as x-ray energy, fh , and wSub as well as guiding the understanding of their effects on image contrast and noise.

In this work, we formalize a generic fast hue-preserving histogram equalization method based on the RGB color space for image contrast enhancement and two versions of that generic process. The first method estimates a RGB 3D histogram to be equalized using R-red, G-green, and B-blue 1D histograms, while the second method employs RG, RB, and GB 2D histograms. The histogram equalization is performed using shift hue-preserving transformations, avoiding unrealistic colors. Our methods have linear time and space complexities with respect to the size of the image and do not need to apply conversions from a color space to another in order to perform the image enhancement. Such design complies with real-time applications requirements. An objective assessment comparing our methods and others is performed using a contrast measure and a color image quality measure, where the quality is established as a weighting of the naturalness and colorfulness indexes. We analyze 300 images from the dataset of the University of Berkeley. Experiments show that the value of the image contrast produced by our methods is in average 50% greater than the original image value, keeping the quality of the produced images close to the original one.

We propose a new variational model in Sobolev-Orlicz spaces with non-standard growth conditions of the objective functional and discuss its applications to the simultaneous contrast enhancement and denoising of color images. The characteristic feature of the proposed model is that we deal with a constrained non-convex minimization problem that lives in variable Sobolev-Orlicz spaces where the variable exponent is unknown a priori and it depends on a particular function that belongs to the domain of the objective functional. In contrast to the standard approach, we do not apply any spatial regularization to the image gradient. We discuss the consistency of the variational model, give the scheme for its regularization, derive the corresponding optimality system, and propose an iterative algorithm for practical implementations.

This paper presents a new approach for automatic image color correction, based on statistical learning. The method both parameterizes color independently of illumination and corrects color for changes of illumination. This is useful in many image processing applications, such as image segmentation or background subtraction. The motivation for using a learning approach is to deal with changes of lighting typical of indoor environments such as home and office. The method is based on learning color invariants using a modified multi‐layer perceptron (MLP). The MLP is odd‐layered. The middle layer includes two neurons which estimate two color invariants and one input neuron which takes in the luminance desired in output of the MLP. The advantage of the modified MLP over a classical MLP is better performance and the estimation of invariants to illumination. The trained modified MLP can be applied using look‐up tables, yielding very fast processing. Results illustrate the approach and compare it with other color correction approaches from the literature. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 132–142, 2007

In order to solve the problems of low brightness contrast of a color image, hiding a large amount of detail information, and deviation of color information in the process of image acquisition, an optimization method of plane image color enhancement processing based on computer vision virtual reality is proposed. In this method, the input RGB image is converted into the image represented by the HSI color model, and its adaptive brightness is adjusted to improve the overall brightness of the image. For the local detail enhancement of the color image, the three-dimensional Gaussian model perceived by retinal neurons is introduced into the illuminance image estimation of the MSR algorithm to enhance the image color. The results are as follows: from the perspective of objective parameter evaluation, the mean, standard deviation, information entropy, and average gradient of example images 1 and 2 are improved by about 70%; this algorithm not only enhances the brightness and contrast of the image but also maintains the detailed edge information of the image and the color characteristics of the object itself. The average enhancement rate is the highest among various algorithms, up to 95%. The algorithm proposed in this paper maintains the edge detail information of the image, optimizes the defects of the combination of traditional bilateral filtering and Retinex algorithm, and the color is also well restored, which makes the monitoring image easier to identify, more conducive to criminal investigation and solving cases, and lays a foundation for subsequent image processing.

We address the problem of contrast enhancement for color images. Methods directly derived from gray-level enhancement such as histogram equalization produce significant artifacts, including severe color shifts. Other enhancement techniques that are derived from the Retinex theory may suffer from strong `halo' effects. Our method to enhance images is inspired from the Retinex theory and tries to mimic human color perception. The method helps in achieving color constancy and also results in color contrast enhancement. We express the intensity as a product of illumination and reflectance and estimate these separately. Enhancement is then applied to the illuminant component only. Non-local means filter is used to estimate the illuminant and then the enhancement of the illumination is performed automatically without any manual intervention and multiplied back by the reflectance to obtain enhancement. We compare our results with those from other enhancement techniques and with results from commercial software packages such as PhotoFlair® that uses multi-scale retinex with color restoration (MSRCR) and Picasa¿ and observe that our results are consistently 'visually better'. Finally, we perform a statistical analysis of our results and quantitatively show that our approach produces effective and substantial image enhancement. This is validated by ratings from human observers.

A novel slime mold algorithm for grayscale and color image contrast enhancement

To realize image processing as hardware module is an effective way to achieve high performance and low power processing. However, hardware development is a heavy burden for software engineer handling image processing usually. High-level synthesis, HLS, converting software program to hardware automatically is a desirable technology for software engineer with less skill of hardware design. On the other hand, the HLS has a significant problem such as it tends to generate a large and low performance hardware when the software program input has never consideration of hardware organization. This paper, focusing on color image contrast enhancement based on histogram equalization, investigates software description methods considering hardware to make HLS generate a good hardware module with high-performance and low power.

Color night vision through fusion of images derived from visible and thermal infrared sensors has come into application. A lot of research of perceptual or subjective evaluation of color fusion algorithms is conducted by the TNO, MIT and NRL[1-5]. Objective evaluation is becoming an increasing issue. A good fusion algorithm should preserve or enhance all of the useful features, i.e., targets pop out and content or details from the source images. Objective: The goal of our research is to predict details and target detectability of color fusion images by measuring image contrast. Methods: A color image contrast metric[11], which was designed to compute how much details for natural images, is employed to measure details and target detectability of color fusion images in this paper. A sub-band contrast index is defined, the first band contrast index indicates the level of details, and higher band contrast indexes indicate the level of target detectability. Results: The perceptual details have high correlation with the first sub-band contrast indexes, and the perceptual target detectability has correlation with higher sub-band contrast indexes. This means the sub-band contrast indexes may predict details and target detectability, however, noise effect on details need to be considered and prediction of target detectability need to be improved.

Illumination optimization, often combined with optical proximity corrections (OPC) to the mask, is becoming one of the critical components for a production-worthy lithography process for 55nm-node DRAM/Flash memory devices and beyond. At low-k₁, e.g. k₁<0.31, both resolution and imaging contrast can be severely limited by the current imaging tools while using the standard illumination sources. Illumination optimization is a process where the source shape is varied, in both profile and intensity distribution, to achieve enhancement in the final image contrast as compared to using the non-optimized sources. The optimization can be done efficiently for repetitive patterns such as DRAM/Flash memory cores. However, illumination optimization often produces source shapes that are "free-form" like and they can be too complex to be directly applicable for production and lack the necessary radial and annular symmetries desirable for the diffractive optical element (DOE) based illumination systems in today's leading lithography tools. As a result, post-optimization rendering and verification of the optimized source shape are often necessary to meet the production-ready or manufacturability requirements and ensure optimal performance gains. In this work, we describe our approach to the illumination optimization for k₁<0.31 DRAM/Flash memory patterns, using an ASML XT:1400i at NA 0.93, where the all necessary manufacturability requirements are fully accounted for during the optimization. The imaging contrast in the resist is optimized in a reduced solution space constrained by the manufacturability requirements, which include minimum distance between poles, minimum opening pole angles, minimum ring width and minimum source filling factor in the sigma space. For additional performance gains, the intensity within the optimized source can vary in a gray-tone fashion (eight shades used in this work). Although this new optimization approach can sometimes produce closely spaced solutions as gauged by the NILS based metrics, we show that the optimal and production-ready source shape solution can be easily determined by comparing the best solutions to the "free-form" solution and more importantly, by their respective imaging fidelity and process latitude ranking. Imaging fidelity and process latitude simulations are performed to analyze the impact and sensitivity of the manufacturability requirements on pattern specific illumination optimizations using ASML XT:1400i and other latest imaging systems. Mask model based OPC (MOPC) is applied and optimized sequentially to ensure that the CD uniformity requirements are met.

The X-ray imaging modality has played an important role in the daily verification of a patient’s position for accurate radiation treatment (RT), especially in modern RT techniques that require highly precise patient localization. Although cone-beam computer tomography (CBCT) has already been introduced to the RT field, two-dimensional (2D) X-ray portal imagers have been more widely used than CBCT for the daily check of patient localization owing to the lower patient exposure and time consumption. However, the 2D imager practically provide a lower bone-tissue contrast than CBCT. Thus, we propose a method that enhances the image contrast of daily acquired 2D images by using just one CBCT scan during an entire RT course. This method operates on the basis of the depth normalization (DN) technique and requires no artificial data manipulation such as image post-processing filters. We implemented the algorithm for the portal images of a cylindrical phantom and three patients. From DN results, the image contrast for the phantom and the patients increased by factors of 26.3 and 13.4 on average, and their contrast-to-noise ratios were maintained at differences of 8.6% and 7.7% on average, respectively. Moreover, the DN method provided a stronger contrast enhancement at lower doses that could suppress the imaging exposure and thus improve the patient’s safety. Therefore, the verification of RT patient localization is expected to be performed more efficiently and accurately by using the DN method.

Polychromatic phase-contrast radiography differs from traditional (absorption-only) radiography in that the method requires at least a partially coherent x-ray source and the resulting images contain information about the phase shifts of x-rays in addition to the traditional absorption information. In a typical embodiment, this effect results in a measurable enhancement in image contrast at the edges of objects. In this study, a phase-contrast imaging system was adapted to allow an object to be imaged at multiple projections, and these projections were used to generate phase-contrast computed tomography images. The images obtained with this technique show edge enhancements surrounding the objects within the image.

This paper comprehensively demonstrates the efficiency of balanced detection in a visible optical coherence tomography instrument employing a low-noise supercontinuum laser. By using an innovative technique for digitally aligning camera pixels, we achieved a noise floor reduction of up to 12.8 dB across the entire imaging depth range, particularly near the zero optical path difference between the interferometer arms. The instrument presented here operates at a central wavelength of 590 nm. It delivers high-resolution images with a sensitivity of up to 74 dB in a single spectrometer configuration and 92.8 dB in a balanced configuration. The enhancement in image contrast is exemplified through images of an optical phantom and in-vivo images of a human thumb and nail.