Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision. Among the existing approaches based on nonlinear histogram transformations, contrast limited adaptive histogram equalization (CLAHE) is a popular choice for dealing with 2D images obtained in natural and scientific settings. The recent hardware upgrade in data acquisition systems results in significant increase in data complexity, including their sizes and dimensions. Measurements of densely sampled data higher than three dimensions, usually composed of 3D data as a function of external parameters, are becoming commonplace in various applications in the natural sciences and engineering. The initial understanding of these complex multidimensional datasets often requires human intervention through visual examination, which may be hampered by the varying levels of contrast permeating through the dimensions. We show both qualitatively and quantitatively that using our multidimensional extension of CLAHE (MCLAHE) acting simultaneously on all dimensions of the datasets allows better visualization and discernment of multidimensional image features, as demonstrated using cases from 4D photoemission spectroscopy and fluorescence microscopy. Our implementation of multidimensional CLAHE in Tensorflow is publicly accessible and supports parallelization with multiple CPUs and various other hardware accelerators, including GPUs.
Evaluating the outcome of contrast enhancement requires quantitative metrics of image contrast, which are rarely used in the early demonstrations of HE algorithms @cite_24 @cite_22 @cite_29 @cite_36 @cite_27 @cite_0 because the use cases are predominantly in 2D and the improvements of image quality are largely intuitive. In domain-specific settings involving higher-dimensional (3D and above) imagery, intuition becomes less suitable for making judgments, but computational contrast metrics can provide guidance for evaluation. The commonly known contrast metrics include the mean squared error (MSE) or the related PSNR @cite_11 @cite_21 , the standard deviation (also called the root-mean-square contrast) @cite_38 and the Shannon entropy (also called the grey-level entropy) @cite_39 . These metrics are naturally generalizable to imagery in arbitrary dimensions @cite_31 and are easy to compute. We also note that despite the recently developed 2D image quality assessment scores based on the current understanding of human visual systems @cite_11 @cite_39 proved to be more effective than the classic metrics we choose to quantify contrast, their generalization and relevance to the evaluation of higher-dimensional images obtained in natural sciences and engineering settings, often without undistorted references, are not yet explored, so they are not used here for comparison of results.
@cite_38 @cite_22 @cite_36 @cite_29 @cite_21 @cite_39 @cite_24 @cite_0 @cite_27 @cite_31 @cite_11
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The physical contrast of simple images such as sinusoidal gratings or a single patch of light on a uniform background is well defined and agrees with the perceived contrast, but this is not so for complex images. Most definitions assign a single contrast value to the whole image, but perceived contrast may vary greatly across the image. Human contrast sensitivity is a function of spatial frequency; therefore the spatial frequency content of an image should be considered in the definition of contrast. In this paper a definition of local band-limited contrast in images is proposed that assigns a contrast value to every point in the image as a function of the spatial frequency band. For each frequency band, the contrast is defined as the ratio of the bandpass-filtered image at that frequency to the low-pass image filtered to an octave below the same frequency (local luminance mean). This definition raises important implications regarding the perception of contrast in complex images and is helpful in understanding the effects of image-processing algorithms on the perceived contrast. A pyramidal image-contrast structure based on this definition is useful in simulating nonlinear, threshold characteristics of spatial vision in both normal observers and the visually impaired. The use of a gray level transformation which transforms a given empirical distribution function of gray level values in an image into a uniform distribution has been used as an image enhancement as well as for a normalization procedure. This transformation produces a discrete variable whose empirical distribution might be expected to be approximately uniform since it is related to the well known distribution transformation. In this correspondence, an extension of the theorem is given which shows that the theorem "almost" holds for an "almost" continuous input distribution. The application of the discrete distribution transformation to computer image enhancement is considered. Abstract : A number of simple and inexpensive enhancement techniques are suggested. These techniques attempt to make use of easily computed local context features to aid in the reassignment of each point's gray level during histogram transformation. (Author) Most image enhancement techniques are not suitable for real-time applications. This paper presents two contrast enhancement techniques that can work at TV rates with fairly simple hardware.© (1976) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. Contrast enhancement algorithms have been evolved through last decades to meet the requirement of its objectives. Actually, there are two main objectives while enhancing the contrast of an image: (i) improve its appearance for visual interpretation and (ii) facilitate increase the performance of subsequent tasks (e.g., image analysis, object detection, and image segmentation). Most of the contrast enhancement techniques are based on histogram modifications, which can be performed globally or locally. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is a method which can overcome the limitations of global approaches by performing local contrast enhancement. However, this method relies on two essential hyperparameters: the number of tiles and the clip limit. An improper hyperparameter selection may heavily decrease the image quality toward its degradation. Considering the lack of methods to efficiently determine these hyperparameters, this article presents a learning-based hyperparameter selection method for the CLAHE technique. The proposed supervised method was built and evaluated using contrast distortions from well-known image quality assessment datasets. Also, we introduce a more challenging dataset containing over 6200 images with a large range of contrast and intensity variations. The results show the efficiency of the proposed approach in predicting CLAHE hyperparameters with up to 0.014 RMSE and 0.935 R2 values. Also, our method overcomes both experimented baselines by enhancing image contrast while keeping its natural aspect. Proper contrast change can improve the perceptual quality of most images, but it has largely been overlooked in the current research of image quality assessment (IQA). To fill this void, we in this paper first report a new large dedicated contrast-changed image database (CCID2014), which includes 655 images and associated subjective ratings recorded from 22 inexperienced observers. We then present a novel reduced-reference image quality metric for contrast change (RIQMC) using phase congruency and statistics information of the image histogram. Validation of the proposed model is conducted on contrast related CCID2014, TID2008, CSIQ and TID2013 databases, and results justify the superiority and efficiency of RIQMC over a majority of classical and state-of-the-art IQA methods. Furthermore, we combine aforesaid subjective and objective assessments to derive the RIQMC based Optimal HIstogram Mapping (ROHIM) for automatic contrast enhancement, which is shown to outperform recently developed enhancement technologies. This paper proposes a scheme for adaptive image-contrast enhancement based on a generalization of histogram equalization (HE). HE is a useful technique for improving image contrast, but its effect is too severe for many purposes. However, dramatically different results can be obtained with relatively minor modifications. A concise description of adaptive HE is set out, and this framework is used in a discussion of past suggestions for variations on HE. A key feature of this formalism is a "cumulation function," which is used to generate a grey level mapping from the local histogram. By choosing alternative forms of cumulation function one can achieve a wide variety of effects. A specific form is proposed. Through the variation of one or two parameters, the resulting process can produce a range of degrees of contrast enhancement, at one extreme leaving the image unchanged, at another yielding full adaptive equalization. Abstract An analysis of the local histogram equalization algorithm is presented. An adaptation of the algorithm is suggested that involves varying the window size over different regions of the image. This enables each region to be enhanced equally. The algorithm has a parameter, S, to control the amount of stretching required. Some results are presented and analysed. Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http: www.cns.nyu.edu spl sim lcv ssim .