Journal ArticleDOI
Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution
TLDR
An automatic transformation technique that improves the brightness of dimmed images via the gamma correction and probability distribution of luminance pixels and uses temporal information regarding the differences between each frame to reduce computational complexity is presented.Abstract:
This paper proposes an efficient method to modify histograms and enhance contrast in digital images. Enhancement plays a significant role in digital image processing, computer vision, and pattern recognition. We present an automatic transformation technique that improves the brightness of dimmed images via the gamma correction and probability distribution of luminance pixels. To enhance video, the proposed image-enhancement method uses temporal information regarding the differences between each frame to reduce computational complexity. Experimental results demonstrate that the proposed method produces enhanced images of comparable or higher quality than those produced using previous state-of-the-art methods.read more
Citations
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Journal ArticleDOI
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images
Jianrui Cai,Shuhang Gu,Lei Zhang +2 more
TL;DR: This paper proposes to use the convolutional neural network (CNN) to train a SICE enhancer, and builds a large-scale multi-exposure image data set, which contains 589 elaborately selected high-resolution multi-Exposure sequences with 4,413 images.
Journal ArticleDOI
The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement
TL;DR: A novel reduced-reference image quality metric for contrast change (RIQMC) is presented using phase congruency and statistics information of the image histogram and results justify the superiority and efficiency of RIQMC over a majority of classical and state-of-the-art IQA methods.
Journal ArticleDOI
Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data
TL;DR: A new no-reference (NR) IQA model is developed and a robust image enhancement framework is established based on quality optimization, which can well enhance natural images, low-contrast images,Low-light images, and dehazed images.
Journal ArticleDOI
Contrast Enhancement-Based Forensics in Digital Images
TL;DR: This paper proposes two novel algorithms to detect the contrast enhancement involved manipulations in digital images, focusing on the detection of global contrast enhancement applied to the previously JPEG-compressed images, which are widespread in real applications.
Journal ArticleDOI
An adaptive gamma correction for image enhancement
Shanto Rahman,Mostafijur Rahman,Mohammad Abdullah-Al-Wadud,Golam Dastegir Al-Quaderi,Mohammad Shoyaib +4 more
TL;DR: An adaptive gamma correction (AGC) is proposed to appropriately enhance the contrast of the image where the parameters of AGC are set dynamically based on the image information.
References
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Journal ArticleDOI
FSIM: A Feature Similarity Index for Image Quality Assessment
TL;DR: A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features.
Journal ArticleDOI
Contrast enhancement using brightness preserving bi-histogram equalization
TL;DR: It is shown mathematically that the proposed algorithm preserves the mean brightness of a given image significantly well compared to typical histogram equalization while enhancing the contrast and, thus, provides a natural enhancement that can be utilized in consumer electronic products.
Journal ArticleDOI
Image enhancement based on equal area dualistic sub-image histogram equalization method
Yu Wang,Qian Chen,Baeomin Zhang +2 more
TL;DR: The simulation results indicate that the algorithm can not only enhance the image information effectively but also preserve the original image luminance well enough to make it possible to be used in a video system directly.
Journal ArticleDOI
A Histogram Modification Framework and Its Application for Image Contrast Enhancement
TL;DR: A general framework based on histogram equalization for image contrast enhancement, and a low-complexity algorithm for contrast enhancement is presented, and its performance is demonstrated against a recently proposed method.
Journal ArticleDOI
Image enhancement via adaptive unsharp masking
TL;DR: A new method for unsharp masking for contrast enhancement of images is presented that employs an adaptive filter that controls the contribution of the sharpening path in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas.
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