Topic
Histogram equalization
About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.
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16 Dec 2008
TL;DR: A novel image equalization technique which is based on singular value decomposition (SVD) and compared with the standard grayscale histogram equalization (GHE) method suggests that the proposed SVE method clearly outperforms the GHE method.
Abstract: In this paper, a novel image equalization technique which is based on singular value decomposition (SVD) is proposed. The singular value matrix represents the intensity information of the given image and any change on the singular values change the intensity of the input image. The proposed technique converts the image into the SVD domain and after normalizing the singular value matrix it reconstructs the image in the spatial domain by using the updated singular value matrix. The technique is called the singular value equalization (SVE) and compared with the standard grayscale histogram equalization (GHE) method. The visual and quantitative results suggest that the proposed SVE method clearly outperforms the GHE method.
124 citations
TL;DR: This paper proposes a novel algorithmic approach of image enhancement via optimal contrast-tone mapping that maximizes expected contrast gain subject to an upper limit on tone distortion and optionally to other constraints that suppress artifacts.
Abstract: This paper proposes a novel algorithmic approach of image enhancement via optimal contrast-tone mapping. In a fundamental departure from the current practice of histogram equalization for contrast enhancement, the proposed approach maximizes expected contrast gain subject to an upper limit on tone distortion and optionally to other constraints that suppress artifacts. The underlying contrast-tone optimization problem can be solved efficiently by linear programming. This new constrained optimization approach for image enhancement is general, and the user can add and fine tune the constraints to achieve desired visual effects. Experimental results demonstrate clearly superior performance of the new approach over histogram equalization and its variants.
123 citations
TL;DR: Experimental results show that the proposed color image watermarking is not only robust against common image processing operations such as filtering, JPEG compression, histogram equalization, and image blurring, but also robust against the geometrical distortions.
123 citations
TL;DR: To suppress the effect of noise for unstable color invariant values, histograms are computed by variable kernel density estimators and it is empirically verified that the proposed density estimator compares favorably to traditional histogram schemes for the purpose of object recognition.
Abstract: An effective object recognition scheme is to represent and match images on the basis of histograms derived from photometric color invariants. A drawback, however, is that certain color invariant values become very unstable in the presence of sensor noise. To suppress the effect of noise for unstable color invariant values, in this paper, histograms are computed by variable kernel density estimators. To apply variable kernel density estimation in a principled way, models are proposed for the propagation of sensor noise through color invariant variables. As a result, the associated uncertainty is obtained for each color invariant value. The associated uncertainty is used to derive the parameterization of the variable kernel for the purpose of robust histogram construction. It is empirically verified that the proposed density estimator compares favorably to traditional histogram schemes for the purpose of object recognition.
121 citations
24 Nov 1998
TL;DR: Multipeak histogram equalization has been proposed, where each detected peak of the histogram is independently equalized to eliminate the effect of brightness saturation and improve the perceptibility of the output image.
Abstract: Contrast enhancement of an image can efficiently be performed by histogram equalization. However, the global histogram equalization will cause an effect on brightness saturation in some almost homogeneous area. Therefore, multipeak histogram equalization has been proposed. Each detected peak of the histogram is independently equalized. The effect of brightness saturation can be eliminated and the perceptibility improved. By using the histogram equalization method, the output image can preserve the mean value brightness of the input image.
120 citations