A general framework based on histogram equalization for image contrast enhancement is presented. In this framework, contrast enhancement is posed as an optimization problem that minimizes a cost function. Histogram equalization is an effective technique for contrast enhancement. However, a conventional histogram equalization (HE) usually results in excessive contrast enhancement, which in turn gives the processed image an unnatural look and creates visual artifacts. By introducing specifically designed penalty terms, the level of contrast enhancement can be adjusted; noise robustness, white/black stretching and mean-brightness preservation may easily be incorporated into the optimization. Analytic solutions for some of the important criteria are presented. Finally, a low-complexity algorithm for contrast enhancement is presented, and its performance is demonstrated against a recently proposed method.

A Histogram Modification Framework and Its Application for Image Contrast Enhancement

A novel contrast enhancement algorithm based on the layered difference representation of 2D histograms is proposed in this paper. We attempt to enhance image contrast by amplifying the gray-level differences between adjacent pixels. To this end, we obtain the 2D histogram h(k, k+l) from an input image, which counts the pairs of adjacent pixels with gray-levels k and k+l, and represent the gray-level differences in a tree-like layered structure. Then, we formulate a constrained optimization problem based on the observation that the gray-level differences, occurring more frequently in the input image, should be more emphasized in the output image. We first solve the optimization problem to derive the transformation function at each layer. We then combine the transformation functions at all layers into the unified transformation function, which is used to map input gray-levels to output gray-levels. Experimental results demonstrate that the proposed algorithm enhances images efficiently in terms of both objective quality and subjective quality.

Contrast Enhancement Based on Layered Difference Representation of 2D Histograms

This paper proposes an algorithm that enhances the contrast of an input image using interpixel contextual information. The algorithm uses a 2-D histogram of the input image constructed using a mutual relationship between each pixel and its neighboring pixels. A smooth 2-D target histogram is obtained by minimizing the sum of Frobenius norms of the differences from the input histogram and the uniformly distributed histogram. The enhancement is achieved by mapping the diagonal elements of the input histogram to the diagonal elements of the target histogram. Experimental results show that the algorithm produces better or comparable enhanced images than four state-of-the-art algorithms.

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Contextual and Variational Contrast Enhancement

This paper presents a new formulation of the regularized image up-sampling problem that incorporates models of the image acquisition and display processes. We give a new analytic perspective that justifies the use of total-variation regularization from a signal processing perspective, based on an analysis that specifies the requirements of edge-directed filtering. This approach leads to a new data fidelity term that has been coupled with a total-variation regularizer to yield our objective function. This objective function is minimized using a level-sets motion that is based on the level-set method, with two types of motion that interact simultaneously. A new choice of these motions leads to a stable solution scheme that has a unique minimum. One aspect of the human visual system, perceptual uniformity, is treated in accordance with the linear nature of the data fidelity term. The method was implemented and has been verified to provide improved results, yielding crisp edges without introducing ringing or other artifacts.

Image up-sampling using total-variation regularization with a new observation model

This paper proposes a new histogram equalization method, called RSWHE (recursively separated and weighted histogram equalization), for brightness preservation and image contrast enhancement. The essential idea of RSWHE is to segment an input histogram into two or more sub-histograms recursively, to modify the sub-histograms by means of a weighting process based on a normalized power law function, and to perform histogram equalization on the weighted sub-histograms independently. RSIHE (recursive sub-image histogram equalization) and RMSHE (recursive mean separate histogram equalization) are some methods similar to RSWHE, but they do not carry out the above weighting process. We show that compared to other existent methods, RSWHE preserves the image brightness more accurately and produces images with better contrast enhancement.

Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement

A fast and effective method for image contrast enhancement is presented. In the proposed method, the probability distribution function (histogram) of an image is modified by weighting and thresholding before the histogram equalization (HE) is performed. We show that such an approach provides a convenient and effective mechanism to control the enhancement process, while being adaptive to various types of images. We also discuss application of the proposed method in video enhancement. Experimental results are presented and compared with results from other contemporary methods.

Fast Image/Video Contrast Enhancement Based on Weighted Thresholded Histogram Equalization

We propose an isophote-oriented, orientation-adaptive interpolation method. The proposed method employs an interpolation kernel that adapts to the local orientation of isophotes, and the pixel values are obtained through an oriented, bilinear interpolation. We show that, by doing so, the curvature of the interpolated isophotes is reduced, and, thus, zigzagging artifacts are largely suppressed. Analysis and experiments show that images interpolated using the proposed method are visually pleasing and almost artifact free

A New Orientation-Adaptive Interpolation Method

A new interpolation-based scheme for image expansion is introduced. In the proposed method, the fidelity and sharpness of edges in the expanded image is emphasized. This is achieved by applying edge-directed interpolation and edge sharpening operations. Compared with other methods, our method provides natural and sharp expanded images with light computational cost.

A new edge-directed image expansion scheme

Blurring effect is among the major visual degradations resulting from digital image enlargement. We introduce a set of sigmoidal functions to sharpen edges of enlarged images. We show that these functions have properties that are especially desirable for sharpening expanded edges. We then develop an adaptive contrast enhancement scheme based on edge sharpening using these functions. It is shown that this scheme results in images of better contrast and natural-looking sharp edges.

Contrast enhancement for enlarged images based on edge sharpening

We introduce a novel image interpolation method, which focuses on providing artifact-free contours. In our method, the image contours are divided into edges and ridges, and we estimate the orientation of them differently and apply directional interpolations on them. Our method gives visually pleasing and natural-looking images. Experiment results are shown and compared with other interpolations methods.

Qing Wang

Papers

Fast Image/Video Contrast Enhancement Based on Weighted Thresholded Histogram Equalization

A New Orientation-Adaptive Interpolation Method

A new edge-directed image expansion scheme

Contrast enhancement for enlarged images based on edge sharpening

A contour-preserving image interpolation method