16,410 research outputs found
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
Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction
As an efficient image contrast enhancement (CE) tool, adaptive gamma
correction (AGC) was previously proposed by relating gamma parameter with
cumulative distribution function (CDF) of the pixel gray levels within an
image. ACG deals well with most dimmed images, but fails for globally bright
images and the dimmed images with local bright regions. Such two categories of
brightness-distorted images are universal in real scenarios, such as improper
exposure and white object regions. In order to attenuate such deficiencies,
here we propose an improved AGC algorithm. The novel strategy of negative
images is used to realize CE of the bright images, and the gamma correction
modulated by truncated CDF is employed to enhance the dimmed ones. As such,
local over-enhancement and structure distortion can be alleviated. Both
qualitative and quantitative experimental results show that our proposed method
yields consistently good CE results
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
An Investigation towards Effectiveness in Image Enhancement Process in MPSoC
Image enhancement has a primitive role in the vision-based applications. It involves the processing of the input image by boosting its visualization for various applications. The primary objective is to filter the unwanted noises, clutters, sharpening or blur. The characteristics such as resolution and contrast are constructively altered to obtain an outcome of an enhanced image in the bio-medical field. The paper highlights the different techniques proposed for the digital enhancement of images. After surveying these methods that utilize Multiprocessor System-on-Chip (MPSoC), it is concluded that these methodologies have little accuracy and hence none of them are efficiently capable of enhancing the digital biomedical images
Efficient Reversible Watermarking Technique with Contrast Enhancement for Color Images
In this paper histogram bin shifting based reversible data hiding algorithm for color images has been proposed. In this technique binary bits are embedded directly by addition and subtraction in two highest bin chosen and this process is repeated in modified histogram. A location map is generated by pre -processing to prevent the unnecessary overflow and underflow. All other pixels except two highest bins are also manipulated for contrast enhancement. Embedding of binary secret data is done on the each color component (Red, Green, and Blue) of color images. Secret Binary data bits are embedded in random permutation manner to secure the data from unauthorized receiver. Extraction of embedded binary bits is done by inverse algorithm of embedding process and original image is recovered by reverse manipulation embedding process. This proposed algorithm provide high embedding capacity with low distortion of original quality of image which may be used in different medical, military and satellite application.
DOI: 10.17762/ijritcc2321-8169.15062
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