Image enhancement technology plays a very important role to improve image quality in image processing. By enhancing some information and restraining other information selectively, it can improve image visual effect. T...Image enhancement technology plays a very important role to improve image quality in image processing. By enhancing some information and restraining other information selectively, it can improve image visual effect. The objective of this work is to implement the image enhancement to gray scale images using different techniques. After the fundamental methods of image enhancement processing are demonstrated, image enhancement algorithms based on space and frequency domains are systematically investigated and compared. The advantage and defect of the above-mentioned algorithms are analyzed. The algorithms of wavelet based image enhancement are also deduced and generalized. Wavelet transform modulus maxima(WTMM) is a method for detecting the fractal dimension of a signal, it is well used for image enhancement. The image techniques are compared by using the mean(μ),standard deviation(?), mean square error(MSE) and PSNR(peak signal to noise ratio). A group of experimental results demonstrate that the image enhancement algorithm based on wavelet transform is effective for image de-noising and enhancement. Wavelet transform modulus maxima method is one of the best methods for image enhancement.展开更多
This paper proposes two unconstrained algorithms, the Steepest Decent (SD)algorithm and the Conjugate Gradient (CG) algorithm, based on a superexcellent cost function. At thesame time, two constrained algorithms which...This paper proposes two unconstrained algorithms, the Steepest Decent (SD)algorithm and the Conjugate Gradient (CG) algorithm, based on a superexcellent cost function. At thesame time, two constrained algorithms which include the Constrained Steepest Decent (CSD) algorithmand the Constrained Conjugate Gradient algorithm (CCG) are deduced subject to a new constraincondition. They are both implemented in unitary transform domain. The computational complexities ofthe constrained algorithms are compared to those of the unconstrained algorithms. Resultingsimulations show their performance comparisons.展开更多
基金Projects(61376076,61274026,61377024)supported by the National Natural Science Foundation of ChinaProjects(12C0108,13C321)supported by the Scientific Research Fund of Hunan Provincial Education Department,ChinaProjects(2013FJ2011,2014FJ2017,2013FJ4232)supported by the Science and Technology Plan Foundation of Hunan Province,China
文摘Image enhancement technology plays a very important role to improve image quality in image processing. By enhancing some information and restraining other information selectively, it can improve image visual effect. The objective of this work is to implement the image enhancement to gray scale images using different techniques. After the fundamental methods of image enhancement processing are demonstrated, image enhancement algorithms based on space and frequency domains are systematically investigated and compared. The advantage and defect of the above-mentioned algorithms are analyzed. The algorithms of wavelet based image enhancement are also deduced and generalized. Wavelet transform modulus maxima(WTMM) is a method for detecting the fractal dimension of a signal, it is well used for image enhancement. The image techniques are compared by using the mean(μ),standard deviation(?), mean square error(MSE) and PSNR(peak signal to noise ratio). A group of experimental results demonstrate that the image enhancement algorithm based on wavelet transform is effective for image de-noising and enhancement. Wavelet transform modulus maxima method is one of the best methods for image enhancement.
文摘This paper proposes two unconstrained algorithms, the Steepest Decent (SD)algorithm and the Conjugate Gradient (CG) algorithm, based on a superexcellent cost function. At thesame time, two constrained algorithms which include the Constrained Steepest Decent (CSD) algorithmand the Constrained Conjugate Gradient algorithm (CCG) are deduced subject to a new constraincondition. They are both implemented in unitary transform domain. The computational complexities ofthe constrained algorithms are compared to those of the unconstrained algorithms. Resultingsimulations show their performance comparisons.
