Exact Histogram Specification Research Articles

SHINE_color: Controlling low-level properties of colorful images

Visual perception combines top-down processes arising from participants individual histories, such as expectations and goals, and bottom-up processes that arise from visual stimuli properties, such as luminance and contrast. The precise control of low-level visual stimuli properties is essential when investigating visual perception. Without it, for instance, investigations of bottom-up processes are virtually impossible and investigations of top-down processes risk major confounds when testing and formulating hypotheses. The SHINE (spectrum, histogram, and intensity normalization and equalization) toolbox for MATLAB [1] allows precise control of images’ Fourier amplitude spectra, the normalizing and scaling of luminance and contrast, and exact histogram specification optimized for perceptual visual quality. Following Willenbockel and Cols (2010) advices, here we present an adaptation of the SHINE toolbox, named SHINE_color, which extends SHINE functionalities by allowing the parametrical manipulation of low-level properties of both static and animated colorful images.•Parametric manipulation of low-level properties of colorful images•Spectrum, histogram, and intensity normalization and equalization

Local Contrast-Based Pixel Ordering for Exact Histogram Specification.

Histogram equalization is one of the basic image processing tasks for contrast enhancement, and its generalized version is histogram specification, which accepts arbitrary shapes of target histograms including uniform distributions for histogram equalization. It is well known that strictly ordered pixels in an image can be voted to any target histogram to achieve exact histogram specification. This paper proposes a method for ordering pixels in an image on the basis of the local contrast of each pixel, where a Gaussian filter without approximation is used to avoid the duplication of pixel values that disturbs the strict pixel ordering. The main idea of the proposed method is that the problem of pixel ordering is divided into small subproblems which can be solved separately, and then the results are merged into one sequence of all ordered pixels. Moreover, the proposed method is extended from grayscale images to color ones in a consistent manner. Experimental results show that the state-of-the-art histogram specification method occasionally produces false patterns, which are alleviated by the proposed method. Those results demonstrate the effectiveness of the proposed method for exact histogram specification.

Implementation of Steganographic Algorithms Based on Exact Histogram Matching and Colour Visual Cryptography

Sensitive secret data transmission through internet has been of great security concern which can be overcome by steganographic methods achieved through secret image sharing. Two novel steganographic secret algorithms based on colour visual cryptography and exact histogram specification is proposed in the present study. The former approach combines colour visual cryptography with a secret key to produce less distorted meaningful share images. A specified histogram acts as the key for the second approach and provides better security and data obscurity compared to conventional approaches. A novel histogram specification method is also proposed which exactly matches the histogram of an image to a specified histogram.

Notice of Removal: Improved exact histogram specification based on the human visual system

Removed.

Histogram-Based Color Transfer for Image Stitching

Color inconsistency often exists between the images to be stitched and will reduce the visual quality of the stitching results. Color transfer plays an important role in image stitching. This kind of technique can produce corrected images which are color consistent. This paper presents a color transfer approach via histogram specification and global mapping. The proposed algorithm can make images share the same color style and obtain color consistency. There are four main steps in this algorithm. Firstly, overlapping regions between a reference image and a test image are obtained. Secondly, an exact histogram specification is conducted for the overlapping region in the test image using the histogram of the overlapping region in the reference image. Thirdly, a global mapping function is obtained by minimizing color differences with an iterative method. Lastly, the global mapping function is applied to the whole test image for producing a color-corrected image. Both the synthetic dataset and real dataset are tested. The experiments demonstrate that the proposed algorithm outperforms the compared methods both quantitatively and qualitatively.

Fast ordering algorithm for exact histogram specification.

This paper provides a fast algorithm to order in a meaningful, strict way the integer gray values in digital (quantized) images. It can be used in any exact histogram specification-based application. Our algorithm relies on the ordering procedure based on the specialized variational approach. This variational method was shown to be superior to all other state-of-the art ordering algorithms in terms of faithful total strict ordering but not in speed. Indeed, the relevant functionals are in general difficult to minimize because their gradient is nearly flat over vast regions. In this paper, we propose a simple and fast fixed point algorithm to minimize these functionals. The fast convergence of our algorithm results from known analytical properties of the model. Our algorithm is equivalent to an iterative nonlinear filtering. Furthermore, we show that a particular form of the variational model gives rise to much faster convergence than other alternative forms. We demonstrate that only a few iterations of this filter yield almost the same pixel ordering as the minimizer. Thus, we apply only few iteration steps to obtain images, whose pixels can be ordered in a strict and faithful way. Numerical experiments confirm that our algorithm outperforms by far its main competitors.

Exact Histogram Specification Considering the Just Noticeable Difference

Exact histogram specification (EHS) transforms the histogram of an input image into the specified histogram. In the conventional EHS techniques, the pixels are first sorted according to their graylevels, and the pixels that have the same graylevel are further differentiated according to the local average of the pixel values and the edge strength. The strictly ordered pixels are then mapped to the desired histogram. However, since the conventional sorting method is inherently dependent on the initial graylevel-based sorting, the contrast enhancement capability of the conventional EHS algorithms is restricted. We propose a modified EHS algorithm considering the just noticeable difference. In the proposed algorithm, the edge pixels are pre-processed such that the output edge pixels obtained by the modified EHS can result in the local contrast enhancement. Moreover, we introduce a new sorting method for the pixels that have the same graylevel. Experimental results show that the proposed algorithm provides better image enhancement performance compared to the conventional EHS algorithms.

Guest Editorial: Scale-Space and Variational Methods

This special issue highlights some recent contributions to scale-space theory and variational methods for mathematical and computational image analysis. A novel approach to dictionary learning for representing large data sets by sparse linear combinations in terms of so-called coresets, is presented in the paper “Learning Big (Image) Data via Coresets for Dictionaries” (DOI 10.1007/s10851-013-0431-x). Bounds on the size and the approximation error of optimal dictionaries are derived, and parallel or online versions of the learning algorithm may scale up to very large data sets. The issue of scale selection for accurate large-scale intersubject image registration is addressed in the paper “Sparse Multi-Scale Diffeomorphic Registration: the Kernel Bundle Framework” (DOI 10.1007/s10851-012-0409-0). The corresponding variational formulation and evolution equations constitute an exiting recent development related to research on diffeomorphism groups, shape theory and computational anatomy. The paper “Exact Histogram Specification for Digital Images Using a Variational Approach” (DOI 10.1007/s10851012-0401-8) studies the problem of transforming a given digital image (i.e. with quantized pixel values) so as to attain

Fully Smoothed ℓ 1-TV Models: Bounds for the Minimizers and Parameter Choice

We consider a class of convex functionals that can be seen as $\mathcal{C}^{1}$ smooth approximations of the l 1-TV model. The minimizers of such functionals were shown to exhibit a qualitatively different behavior compared to the nonsmooth l 1-TV model (Nikolova et al. in Exact histogram specification for digital images using a variational approach, 2012). Here we focus on the way the parameters involved in these functionals determine the features of the minimizers $\hat{u}$ . We give explicit relationships between the minimizers and these parameters. Given an input digital image f, we prove that the error $\|\hat{u}- f\| _{\infty}$ obeys $b-\varepsilon\leq\|\hat{u}-f\|_{\infty}\leq b$ where b is a constant independent of the input image. Further we can set the parameters so that ?>0 is arbitrarily close to zero. More precisely, we exhibit explicit formulae relating the model parameters, the input image f and the values b and ?. Conversely, we can fix the parameter values so that the error $\|\hat{u}- f\|_{\infty}$ meets some prescribed b,?. All theoretical results are confirmed using numerical tests on natural digital images of different sizes with disparate content and quality.

Exact Histogram Specification for Digital Images Using a Variational Approach

We consider the problem of exact histogram specification for digital (quantized) images. The goal is to transform the input digital image into an output (also digital) image that follows a prescribed histogram. Classical histogram modification methods are designed for real-valued images where all pixels have different values, so exact histogram specification is straightforward. Digital images typically have numerous pixels which share the same value. If one imposes the prescribed histogram to a digital image, usually there are numerous ways of assigning the prescribed values to the quantized values of the image. Therefore, exact histogram specification for digital images is an ill-posed problem. In order to guarantee that any prescribed histogram will be satisfied exactly, all pixels of the input digital image must be rearranged in a strictly ordered way. Further, the obtained strict ordering must faithfully account for the specific features of the input digital image. Such a task can be realized if we are able to extract additional representative information (called auxiliary attributes) from the input digital image. This is a real challenge in exact histogram specification for digital images. We propose a new method that efficiently provides a strict and faithful ordering for all pixel values. It is based on a well designed variational approach. Noticing that the input digital image contains quantization noise, we minimize a specialized objective function whose solution is a real-valued image with slightly reduced quantization noise, which remains very close to the input digital image. We show that all the pixels of this real-valued image can be ordered in a strict way with a very high probability. Then transforming the latter image into another digital image satisfying a specified histogram is an easy task. Numerical results show that our method outperforms by far the existing competing methods.

Automatic Exact Histogram Specification for Contrast Enhancement and Visual System Based Quantitative Evaluation

Histogram equalization, which aims at information maximization, is widely used in different ways to perform contrast enhancement in images. In this paper, an automatic exact histogram specification technique is proposed and used for global and local contrast enhancement of images. The desired histogram is obtained by first subjecting the image histogram to a modification process and then by maximizing a measure that represents increase in information and decrease in ambiguity. A new method of measuring image contrast based upon local band-limited approach and center-surround retinal receptive field model is also devised in this paper. This method works at multiple scales (frequency bands) and combines the contrast measures obtained at different scales using L(p)-norm. In comparison to a few existing methods, the effectiveness of the proposed automatic exact histogram specification technique in enhancing contrasts of images is demonstrated through qualitative analysis and the proposed image contrast measure based quantitative analysis.

Controlling low-level image properties: The SHINE toolbox

Visual perception can be influenced by top-down processes related to the observer's goals and expectations, as well as by bottom-up processes related to low-level stimulus attributes, such as luminance, contrast, and spatial frequency. When using different physical stimuli across psychological conditions, one faces the problem of disentangling the contributions of low- and high-level factors. Here, we make available the SHINE (spectrum, histogram, and intensity normalization and equalization) toolbox for MATLAB, which we have found useful for controlling a number of image properties separately or simultaneously. The toolbox features functions for specifying the (rotational average of the) Fourier amplitude spectra, for normalizing and scaling mean luminance and contrast, and for exact histogram specification optimized for perceptual visual quality. SHINE can thus be employed for parametrically modifying a number of image properties or for equating them across stimuli to minimize potential low-level confounds in studies on higher level processes.

Exact global histogram specification optimized for structural similarity

An exact histogram specification (EHS) method modifies its input image to have a specified histogram. Applications of EHS include image (contrast) enhancement (e.g., by histogram equalization) and histogram watermarking. Performing EHS on an image, however, reduces its visual quality. Starting from the output of a generic EHS method, we maximize the structural similarity index (SSIM) between the original image (before EHS) and the result of EHS iteratively. Essential in this process is the computationally simple and accurate formula we derive for SSIM gradient. As it is based on gradient ascent, the proposed EHS always converges. Experimental results confirm that while obtaining the histogram exactly as specified, the proposed method invariably outperforms the existing methods in terms of visual quality of the result. The computational complexity of the proposed method is shown to be of the same order as that of the existing methods. Index terms: histogram modification, histogram equalization, optimization for perceptual visual quality, structural similarity gradient ascent, histogram watermarking, contrast enhancement.

Automated compensation of light attenuation in confocal microscopy by exact histogram specification

Confocal laser scanning microscopy (CLSM) enables us to capture images representing optical sections on the volume of a specimen. The images acquired from different layers have a different contrast: the images obtained from the deeper layers of the specimen will have a lower contrast with respect to the images obtained from the topmost layers. The main reasons responsible for the effects described above are light absorption and scattering by the atoms and molecules contained in the volume through which the light passes. Also light attenuation can be caused by the inclination of the observed surface. In the case of the surfaces that have a steep inclination, the reflected light will have a different direction than the one of the detector. We propose a technique of digital image processing that can be used to compensate the effects of light attenuation based on histogram operations. We process the image series obtained by CLSM by exact histogram specification and equalization. In this case, a strict ordering among pixels must be induced in order to achieve the exact histogram modeling. The processed images will end up having exactly the specified histogram and not a histogram with a shape that just resembles to the specified one, as in the case of classical histogram specification algorithms. Experimental results and theoretical aspects of the induced ordering are discussed, as well as a comparison between several histogram modeling techniques with respect to the processing of image series obtained by confocal microscopy.

Flattest histogram specification with accurate brightness preservation

Brightness preservation is in great demand in the consumer electronics field, when the image is effectively enhanced. Finding the flattest target histogram for contrast enhancement subject to a constrained mean brightness is proposed. After that an exact histogram specification method is employed to help meet perfect brightness preservation. The authors name such a method as flattest histogram specification with accurate brightness preservation (FHSABP). Comparing with the existing methods, experiments show that FHSABP can not only enhance the image effectively, but also preserve the brightness quite well, and so it is possible for it to be utilised in consumer electronic products.

Joint Exact Histogram Specification and Image Enhancement Through the Wavelet Transform

Histogram specification (equalization) is an important tool for tasks such as image enhancement and normalization. Although this problem has exact solution for continuous images, in the case of digital images, it is ill posed. Recently, an exact pixel ordering method based solely on local image intensity was proposed, yet still with some limitations, especially since it ignores the important image edge information. In this paper, we present a wavelet-based method that simultaneously achieves the exact histogram specification and good image enhancement performance. It does so through a carefully designed strict pixel ordering process, during which the wavelet coefficients are fine tuned for the image enhancement purpose. Compared to previous work, this approach takes into account not only local mean intensity values, but also local edge information. Other advantages include fast pixel ordering, good statistical models, and better image enhancement performance. Experimental results and comparison with state-of-the-art methods are presented.

Exact histogram specification

While in the continuous case, statistical models of histogram equalization/specification would yield exact results, their discrete counterparts fail. This is due to the fact that the cumulative distribution functions one deals with are not exactly invertible. Otherwise stated, exact histogram specification for discrete images is an ill-posed problem. Invertible cumulative distribution functions are obtained by translating the problem in a K-dimensional space and further inducing a strict ordering among image pixels. The proposed ordering refines the natural one. Experimental results and statistical models of the induced ordering are presented and several applications are discussed: image enhancement, normalization, watermarking, etc.