Acceleration of Histogram-Based Contrast Enhancement via Selective Downsampling

In this paper, we propose a general framework to accelerate the universal histogram-based image contrast enhancement (CE) algorithms. Both spatial and gray-level selective down- sampling of digital images are adopted to decrease computational cost, while the visual quality of enhanced images is still preserved and without apparent degradation. Mapping function calibration is novelly proposed to reconstruct the pixel mapping on the gray levels missed by downsampling. As two case studies, accelerations of histogram equalization (HE) and the state-of-the-art global CE algorithm, i.e., spatial mutual information and PageRank (SMIRANK), are presented detailedly. Both quantitative and qualitative assessment results have verified the effectiveness of our proposed CE acceleration framework. In typical tests, computational efficiencies of HE and SMIRANK have been speeded up by about 3.9 and 13.5 times, respectively.Comment: accepted by IET Image Processin

Exact Histogram Specification Optimized for Structural Similarity

An exact histogram specification (EHS) method modifies its input image to have a specified histogram. Applications of EHS include image (contrast) enhancement (e.g., by histogram equalization) and histogram watermarking. Performing EHS on an image, however, reduces its visual quality. Starting from the output of a generic EHS method, we maximize the structural similarity index (SSIM) between the original image (before EHS) and the result of EHS iteratively. Essential in this process is the computationally simple and accurate formula we derive for SSIM gradient. As it is based on gradient ascent, the proposed EHS always converges. Experimental results confirm that while obtaining the histogram exactly as specified, the proposed method invariably outperforms the existing methods in terms of visual quality of the result. The computational complexity of the proposed method is shown to be of the same order as that of the existing methods. Index terms: histogram modification, histogram equalization, optimization for perceptual visual quality, structural similarity gradient ascent, histogram watermarking, contrast enhancement

Image Enhancement with Statistical Estimation

Contrast enhancement is an important area of research for the image analysis. Over the decade, the researcher worked on this domain to develop an efficient and adequate algorithm. The proposed method will enhance the contrast of image using Binarization method with the help of Maximum Likelihood Estimation (MLE). The paper aims to enhance the image contrast of bimodal and multi-modal images. The proposed methodology use to collect mathematical information retrieves from the image. In this paper, we are using binarization method that generates the desired histogram by separating image nodes. It generates the enhanced image using histogram specification with binarization method. The proposed method has showed an improvement in the image contrast enhancement compare with the other image.Comment: 9 pages,6 figures; ISSN:0975-5578 (Online); 0975-5934 (Print

Diffusion Tensor Imaging: on the assessment of data quality - a preliminary bootstrap analysis

In the field of nuclear magnetic resonance imaging, diffusion tensor imaging (DTI) has proven an important method for the characterisation of ultrastructural tissue properties. Yet various technical and biological sources of signal uncertainty may prolong into variables derived from diffusion weighted images and thus compromise data validity and reliability. To gain an objective quality rating of real raw data we aimed at implementing the previously described bootstrap methodology (Efron, 1979) and investigating its sensitivity to a selection of extraneous influencing factors. We applied the bootstrap method on real DTI data volumes of six volunteers which were varied by different acquisition conditions, smoothing and artificial noising. In addition a clinical sample group of 46 Multiple Sclerosis patients and 24 healthy controls were investigated. The response variables (RV) extracted from the histogram of the confidence intervals of fractional anisotropy were mean width, peak position and height. The addition of noising showed a significant effect when exceeding about 130% of the original background noise. The application of an edge-preserving smoothing algorithm resulted in an inverse alteration of the RV. Subject motion was also clearly depicted whereas its prevention by use of a vacuum device only resulted in a marginal improvement. We also observed a marked gender-specific effect in a sample of 24 healthy control subjects the causes of which remained unclear. In contrary to this the mere effect of a different signal intensity distribution due to illness (MS) did not alter the response variables

Adaptive transfer functions: improved multiresolution visualization of medical models

The final publication is available at Springer via http://dx.doi.org/10.1007/s00371-016-1253-9Medical datasets are continuously increasing in size. Although larger models may be available for certain research purposes, in the common clinical practice the models are usually of up to 512x512x2000 voxels. These resolutions exceed the capabilities of conventional GPUs, the ones usually found in the medical doctors’ desktop PCs. Commercial solutions typically reduce the data by downsampling the dataset iteratively until it fits the available target specifications. The data loss reduces the visualization quality and this is not commonly compensated with other actions that might alleviate its effects. In this paper, we propose adaptive transfer functions, an algorithm that improves the transfer function in downsampled multiresolution models so that the quality of renderings is highly improved. The technique is simple and lightweight, and it is suitable, not only to visualize huge models that would not fit in a GPU, but also to render not-so-large models in mobile GPUs, which are less capable than their desktop counterparts. Moreover, it can also be used to accelerate rendering frame rates using lower levels of the multiresolution hierarchy while still maintaining high-quality results in a focus and context approach. We also show an evaluation of these results based on perceptual metrics.Peer ReviewedPostprint (author's final draft

Wavelet-Based Enhancement Technique for Visibility Improvement of Digital Images

Image enhancement techniques for visibility improvement of color digital images based on wavelet transform domain are investigated in this dissertation research. In this research, a novel, fast and robust wavelet-based dynamic range compression and local contrast enhancement (WDRC) algorithm to improve the visibility of digital images captured under non-uniform lighting conditions has been developed. A wavelet transform is mainly used for dimensionality reduction such that a dynamic range compression with local contrast enhancement algorithm is applied only to the approximation coefficients which are obtained by low-pass filtering and down-sampling the original intensity image. The normalized approximation coefficients are transformed using a hyperbolic sine curve and the contrast enhancement is realized by tuning the magnitude of the each coefficient with respect to surrounding coefficients. The transformed coefficients are then de-normalized to their original range. The detail coefficients are also modified to prevent edge deformation. The inverse wavelet transform is carried out resulting in a lower dynamic range and contrast enhanced intensity image. A color restoration process based on the relationship between spectral bands and the luminance of the original image is applied to convert the enhanced intensity image back to a color image. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some pathological scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for tackling the color constancy problem. The illuminant is modeled having an effect on the image histogram as a linear shift and adjust the image histogram to discount the illuminant. The WDRC algorithm is then applied with a slight modification, i.e. instead of using a linear color restoration, a non-linear color restoration process employing the spectral context relationships of the original image is applied. The proposed technique solves the color constancy issue and the overall enhancement algorithm provides attractive results improving visibility even for scenes with near-zero visibility conditions. In this research, a new wavelet-based image interpolation technique that can be used for improving the visibility of tiny features in an image is presented. In wavelet domain interpolation techniques, the input image is usually treated as the low-pass filtered subbands of an unknown wavelet-transformed high-resolution (HR) image, and then the unknown high-resolution image is produced by estimating the wavelet coefficients of the high-pass filtered subbands. The same approach is used to obtain an initial estimate of the high-resolution image by zero filling the high-pass filtered subbands. Detail coefficients are estimated via feeding this initial estimate to an undecimated wavelet transform (UWT). Taking an inverse transform after replacing the approximation coefficients of the UWT with initially estimated HR image, results in the final interpolated image. Experimental results of the proposed algorithms proved their superiority over the state-of-the-art enhancement and interpolation techniques