70,875 research outputs found
An Improved Approach for Contrast Enhancement of Spinal Cord Images based on Multiscale Retinex Algorithm
This paper presents a new approach for contrast enhancement of spinal cord
medical images based on multirate scheme incorporated into multiscale retinex
algorithm. The proposed work here uses HSV color space, since HSV color space
separates color details from intensity. The enhancement of medical image is
achieved by down sampling the original image into five versions, namely, tiny,
small, medium, fine, and normal scale. This is due to the fact that the each
versions of the image when independently enhanced and reconstructed results in
enormous improvement in the visual quality. Further, the contrast stretching
and MultiScale Retinex (MSR) techniques are exploited in order to enhance each
of the scaled version of the image. Finally, the enhanced image is obtained by
combining each of these scales in an efficient way to obtain the composite
enhanced image. The efficiency of the proposed algorithm is validated by using
a wavelet energy metric in the wavelet domain. Reconstructed image using
proposed method highlights the details (edges and tissues), reduces image noise
(Gaussian and Speckle) and improves the overall contrast. The proposed
algorithm also enhances sharp edges of the tissue surrounding the spinal cord
regions which is useful for diagnosis of spinal cord lesions. Elaborated
experiments are conducted on several medical images and results presented show
that the enhanced medical pictures are of good quality and is found to be
better compared with other researcher methods.Comment: 13 pages, 6 figures, International Journal of Imaging and Robotics.
arXiv admin note: text overlap with arXiv:1406.571
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
Design of Novel Algorithm and Architecture for Gaussian Based Color Image Enhancement System for Real Time Applications
This paper presents the development of a new algorithm for Gaussian based
color image enhancement system. The algorithm has been designed into
architecture suitable for FPGA/ASIC implementation. The color image enhancement
is achieved by first convolving an original image with a Gaussian kernel since
Gaussian distribution is a point spread function which smoothen the image.
Further, logarithm-domain processing and gain/offset corrections are employed
in order to enhance and translate pixels into the display range of 0 to 255.
The proposed algorithm not only provides better dynamic range compression and
color rendition effect but also achieves color constancy in an image. The
design exploits high degrees of pipelining and parallel processing to achieve
real time performance. The design has been realized by RTL compliant Verilog
coding and fits into a single FPGA with a gate count utilization of 321,804.
The proposed method is implemented using Xilinx Virtex-II Pro XC2VP40-7FF1148
FPGA device and is capable of processing high resolution color motion pictures
of sizes of up to 1600x1200 pixels at the real time video rate of 116 frames
per second. This shows that the proposed design would work for not only still
images but also for high resolution video sequences.Comment: 15 pages, 15 figure
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Low-end and compact mobile cameras demonstrate limited photo quality mainly
due to space, hardware and budget constraints. In this work, we propose a deep
learning solution that translates photos taken by cameras with limited
capabilities into DSLR-quality photos automatically. We tackle this problem by
introducing a weakly supervised photo enhancer (WESPE) - a novel image-to-image
Generative Adversarial Network-based architecture. The proposed model is
trained by under weak supervision: unlike previous works, there is no need for
strong supervision in the form of a large annotated dataset of aligned
original/enhanced photo pairs. The sole requirement is two distinct datasets:
one from the source camera, and one composed of arbitrary high-quality images
that can be generally crawled from the Internet - the visual content they
exhibit may be unrelated. Hence, our solution is repeatable for any camera:
collecting the data and training can be achieved in a couple of hours. In this
work, we emphasize on extensive evaluation of obtained results. Besides
standard objective metrics and subjective user study, we train a virtual rater
in the form of a separate CNN that mimics human raters on Flickr data and use
this network to get reference scores for both original and enhanced photos. Our
experiments on the DPED, KITTI and Cityscapes datasets as well as pictures from
several generations of smartphones demonstrate that WESPE produces comparable
or improved qualitative results with state-of-the-art strongly supervised
methods
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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