7,200 research outputs found
Image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization
Image enhancement aims at processing an input
image so that the visual content of the output image is more
pleasing or more useful for certain applications. Although
histogram equalization is widely used in image enhancement due
to its simplicity and effectiveness, it changes the mean brightness
of the enhanced image and introduces a high level of noise and
distortion. To address these problems, this paper proposes
image enhancement using fuzzy intensity measure and adaptive
clipping histogram equalization (FIMHE). FIMHE uses fuzzy
intensity measure to first segment the histogram of the original
image, and then clip the histogram adaptively in order to
prevent excessive image enhancement. Experiments on the
Berkeley database and CVF-UGR-Image database show that
FIMHE outperforms state-of-the-art histogram equalization
based methods
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
Automatic detection of welding defects using the convolutional neural network
Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects
- …