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.

/pdf/contextual-and-variational-contrast-enhancement-100rq2rd1u.pdf

Contextual and Variational Contrast Enhancement

In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.

/pdf/automatic-image-equalization-and-contrast-enhancement-using-565zoy3krh.pdf

Automatic Image Equalization and Contrast Enhancement Using Gaussian Mixture Modeling

서브블록 히스토그램 등화기법을 이용한 개선된 콘트래스트 강화 알고리즘 ( An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization )

Volleyball Premier League Algorithm

Two-dimensional histogram equalization and contrast enhancement

An image contrast enhancement method based on genetic algorithm

New robust QCA D flip flop and memory structures

The most important mathematical operation is addition. Other operations such as subtraction, multiplication and division are usually implemented by adders. An efficient adder can be of great assistance in designing arithmetic circuits. QCA is a promising technology which seems to be a good candidate for the next generation of digital systems. So, an efficient QCA full-adder will facilitate creating QCA computational and arithmetic systems. In this paper, two high performances QCA full-adders are presented. They have a very dense structure and constructed using new kinds of five-input majority gates. One of the proposed designs has a robust structure. In this design the presented design rules for constructing a robust QCA circuit have been considered. In contrast to the previous designs constructed using a five-input majority gate, in the proposed QCA full-adders the outputs come out from the same side of the circuit. Also, the input and output signals are not surrounded by the other cells and can easily be accessed. The proposed robust QCA full-adder dominates all the previous robust designs in terms of area, delay and complexity. Using this design, ripple carry adders with different word sizes (that is, 4, 8 and 16) are constructed. In this paper, QCA designer, a common QCA layout design and verification tool is employed to verify and simulate the proposed five-input majority gates and QCA full-adders.

	 

	Key words: Quantum-dot cellular automata, full-adder, five-input majority gate, robustness, ripple carry adder.

https://www.academicjournals.org/article/article1380790030_Hashemi et al.pdf

Sara Hashemi

Papers

An image contrast enhancement method based on genetic algorithm

New robust QCA D flip flop and memory structures

An efficient quantum-dot cellular automata full-adder

A Novel Robust QCA Full-adder☆

New Quantum Dot Cellular Automata Cell Arrangements