Search results
Results From The WOW.Com Content Network
Adaptive histogram equalization (AHE) is a computer image processing technique used to improve contrast in images. It differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the lightness values of the image.
A major drawback to application of the algorithm is an inherent reduction in overall image contrast produced by the operation. [1] When utilized for image enhancement, the difference of Gaussians algorithm is typically applied when the size ratio of kernel (2) to kernel (1) is 4:1 or 5:1.
The stationary wavelet transform (SWT) [1] is a wavelet transform algorithm designed to overcome the lack of translation-invariance of the discrete wavelet transform (DWT). ). Translation-invariance is achieved by removing the downsamplers and upsamplers in the DWT and upsampling the filter coefficients by a factor of () in the th level of the alg
Complete Java code for a 1-D and 2-D DWT using Haar, Daubechies, Coiflet, and Legendre wavelets is available from the open source project: JWave. Furthermore, a fast lifting implementation of the discrete biorthogonal CDF 9/7 wavelet transform in C, used in the JPEG 2000 image compression standard can be found here (archived 5 March 2012).
2 triangles, example to show how fractal compression works. Fractal compression is a lossy compression method for digital images, based on fractals.The method is best suited for textures and natural images, relying on the fact that parts of an image often resemble other parts of the same image. [1]
Image enhancement techniques (like contrast stretching or de-blurring by a nearest neighbor procedure) provided by imaging packages use no a priori model of the process that created the image. With image enhancement noise can effectively be removed by sacrificing some resolution, but this is not acceptable in many applications.
After every image has been processed through the inception architecture, the means and covariances of the activation of the last layer on the two datasets are compared with the distance ((,), (′, ′)) = ‖ ′ ‖ + (+ ′ (′)) Higher distances indicate a poorer generative model. A score of 0 indicates a perfect model.
All pixels of a particular value in the original image must be transformed to just one value in the output image. Exact histogram matching is the problem of finding a transformation for a discrete image so that its histogram exactly matches the specified histogram. [4] Several techniques have been proposed for this.