22,249 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
Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets
In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866
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