Abstract-The limitations of the hardware and dynamic range of digital camera have created the demand for post processing software tool to improve image quality. Image enhancement is a technique that helps to improve finer details of the image. This paper presents a new algorithm for contrast enhancement, where the enhancement rate is controlled by clipped histogram approach, which uses standard intensity deviation. Here standard intensity deviation is used to divide and equalize the image histogram. The equalization processes is applied to sub images independently and combine them into one complete enhanced image. The conventional histogram equalization stretches the dynamic range which leads to a large gap between adjacent pixels that produces over enhancement problem. This drawback is overcome by defining standard intensity deviation value to split and equalize the histogram. The selection of suitable threshold value for clipping and splitting image, provides better enhancement over other methods. The simulation results show that proposed method out performs other conventional histogram equalization (HE) methods and effectively preserves entropy.
A new approach based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for medical image contrast enhancement is presented. The contrast of the input image is enhanced by proposed fusion technique between wavelet based masking using Cuckoo Search Algorithm (CSA) and mask based singular value decomposition. Resolution is enhanced by combining interpolated high frequency sub band and maximum value fused low frequency sub band using mask technique. Experimental results are tested by measuring Peak Signal to Noise Ratio (PSNR) with other contrast and resolution enhancement techniques. Structural Similarity Index of Measure (SSIM) and Correlation coefficient is also evaluated to encourage the proposed method.
The proposed method addresses the general issues of image contrast enhancement. The input image is enhanced by incorporating discrete wavelet transform, singular value decomposition, standard intensity deviation based clipped sub image histogram equalization and masking technique. In this method, low pass filtered coefficients of wavelet and its scaled version undergoes masking approach. The scale value is obtained using singular value decomposition between reconstructed approximation coefficients and standard intensity deviation based clipped sub image histogram equalization image. The masking image is added to the original image to produce a maximum contrast-enhanced image. The supremacy of the proposed method tested over other methods. The qualitative and quantitative analysis is used to justify the performance of the proposed method.
Image enhancement techniques are prominently used to analyze the image by enhancing key factors like contrast, resolution, and quality of the image. The proper analysis of images, it is desirable to pre-process the image for resolution and contrast enhancement. We present here a new approach based on discrete wavelet transform (DWT), singular value decomposition (SVD) for image contrast and resolution enhancement, The contrast of the image is enhanced by maximum value fusion technique applied to the images created by using modified cuckoo search algorithm (CSA) and singular value decomposition separately. The masking approach is employed, for obtaining residual pixel value between original and scaled images independently. Resolution of the image is enhanced by combining interpolated highfrequency sub-band and maximum value fusion image. The proposed algorithm helps to minimize the noise artifacts and over enhancement problems. Experimental results are tested in terms of peak signal to noise ratio (PSNR) and absolute mean brightness error (AMBE). The proposed method shows better performance compared to other contrast and resolution enhancement techniques.
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