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Wan Nural Jawahir Hj Wan Yussof

Bio: Wan Nural Jawahir Hj Wan Yussof is an academic researcher from Universiti Malaysia Terengganu. The author has contributed to research in topics: Image segmentation & Computer science. The author has an hindex of 6, co-authored 25 publications receiving 323 citations. Previous affiliations of Wan Nural Jawahir Hj Wan Yussof include University of Freiburg & Universiti Sains Malaysia.

Papers
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Proceedings ArticleDOI
30 May 2013
TL;DR: Experimental results show that the proposed approach significantly improves the visual quality of underwater images by enhancing contrast, as well as reducing noise and artifacts.
Abstract: Within the last decades, improving the quality of an underwater image has received considerable attention due to poor visibility of the image which is caused by physical properties of the water medium. This paper presents a new method called mixture Contrast Limited Adaptive Histogram Equalization (CLAHE) color models that specifically developed for underwater image enhancement. The method operates CLAHE on RGB and HSV color models and both results are combined together using Euclidean norm. The underwater images used in this study were taken from Redang Island and Bidong Island in Terengganu, Malaysia. Experimental results show that the proposed approach significantly improves the visual quality of underwater images by enhancing contrast, as well as reducing noise and artifacts.

285 citations

Proceedings ArticleDOI
01 Oct 2013
TL;DR: The experimental results show that the proposed CBIR using SIFT algorithm producing excellent retrieval result for images with many corners as compared to retrieving image with less corners.
Abstract: This paper presents an alternative approach for Content Based Image Retrieval (CBIR) using Scale Invariant Feature Transform (SIFT) algorithm for binary and gray scale images. The motivation to use SIFT algorithm for CBIR is due to the fact that SIFT is invariant to scale, rotation and translation as well as partially invariant to affine distortion and illumination changes. Inspired by these facts, this paper investigates the fundamental properties of SIFT for robust CBIR by using MPEG-7, COIL-20 and ZuBuD image databases. Our approach uses detected keypoints and its descriptors to match between the query images and images from the database. Our experimental results show that the proposed CBIR using SIFT algorithm producing excellent retrieval result for images with many corners as compared to retrieving image with less corners.

31 citations

Journal ArticleDOI
TL;DR: Experimental results show that the proposed scale and rotational invariant interest points detector outperforms other methods under different viewpoint angles and provides a comparable results to the existing methods under scale changes for a set of test images with different geometric transformations.

12 citations

Proceedings ArticleDOI
04 Dec 2009
TL;DR: A liver segmentation algorithm is proposed using hybrid techniques by combining morphological-based, region-based and histogram-based techniques to segment volumetric CT data.
Abstract: The first step for computer-aided diagnosis for liver of CT scans is the identification of liver region. To deal with multislice CT scans, automatic liver segmentation is required. In this paper, we propose a liver segmentation algorithm using hybrid techniques by combining morphological-based, region-based and histogram-based techniques to segment volumetric CT data. A morphological-based technique is used to find the initial liver tissue from the first slice which is set as a starting slice and region-based is used for further processing for the rest slices, which incorporates seed point generation from Euclidean distance transform (EDT) image on the previous slice for region growing on the current slice. In order to remove neighboring abdominal organs of the liver which connect to the liver organ, the histogram-based technique is used by finding the left and right histogram tail threshold (HTT) and we repeat the use of morphology filtering and large contour detecting for liver smoothing.

11 citations

Journal ArticleDOI
TL;DR: A fully 3D algorithm for automatic liver segmentation from CT volumetric datasets is presented that has been evaluated on 10 CT scans of the liver and the results are encouraging to poor quality of images.
Abstract: Here a fully 3D algorithm for automatic liver segmentation from CT volumetric datasets is presented. The algorithm starts by smoothing the original volume using anisotropic diffusion. The coarse liver region is obtained from the threshold process that is based on a priori knowledge. Then, several morphological operations is performed such as operating the liver to detach the unwanted region connected to the liver and finding the largest component using the connected component labeling (CCL) algorithm. At this stage, both 3D and 2D CCL is done subsequently. However, in 2D CCL, the adjacent slices are also affected from current slice changes. Finally, the boundary of the liver is refined using graph-cuts solver. Our algorithm does not require any user interaction or training datasets to be used. The algorithm has been evaluated on 10 CT scans of the liver and the results are encouraging to poor quality of images.

8 citations


Cited by
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Journal ArticleDOI
01 Feb 2015
TL;DR: Qualitative analysis reveals that the proposed method significantly enhances the image contrast, reduces the blue-green effect, and minimizes under- and over-enhanced areas in the output image.
Abstract: Method to increase the contrast and reduce the noise of underwater image.Applied histogram modification of integrated RGB and HSV color models.Mapping the image histogram according to Rayleigh distribution.Limiting the dynamic range of color models to reduce under- and over-enhanced areas.Outperforms other state-of-the-art methods in term of contrast and noise reduction. The physical properties of water cause light-induced degradation of underwater images. Light rapidly loses intensity as it travels in water, depending on the color spectrum wavelength. Visible light is absorbed at the longest wavelength first. Red and blue are the most and least absorbed, respectively. Underwater images with low contrast are captured due to the degradation effects of light spectrum. Therefore, the valuable information from these images cannot be fully extracted for further processing. The current study proposes a new method to improve the contrast and reduce the noise of underwater images. The proposed method integrates the modification of image histogram into two main color models, Red-Green-Blue (RGB) and Hue-Saturation-Value (HSV). In the RGB color model, the histogram of the dominant color channel (i.e., blue channel) is stretched toward the lower level, with a maximum limit of 95%, whereas the inferior color channel (i.e., red channel) is stretched toward the upper level, with a minimum limit of 5%. The color channel between the dominant and inferior color channels (i.e., green channel) is stretched to both directions within the whole dynamic range. All stretching processes in the RGB color model are shaped to follow the Rayleigh distribution. The image is converted into the HSV color model, wherein the S and V components are modified within the limit of 1% from the minimum and maximum values. Qualitative analysis reveals that the proposed method significantly enhances the image contrast, reduces the blue-green effect, and minimizes under- and over-enhanced areas in the output image. For quantitative analysis, the test with 300 underwater images shows that the proposed method produces average mean square error (MSE) and peak signal to noise ratio (PSNR) of 76.76 and 31.13, respectively, which outperform six state-of-the-art methods.

208 citations

Book ChapterDOI
01 Jan 2016
TL;DR: This chapter introduces basic notation and mathematical concepts for detecting and describing image features, and discusses properties of perfect features and gives an overview of various existing detection and description methods.
Abstract: Feature detection, description and matching are essential components of various computer vision applications, thus they have received a considerable attention in the last decades. Several feature detectors and descriptors have been proposed in the literature with a variety of definitions for what kind of points in an image is potentially interesting (i.e., a distinctive attribute). This chapter introduces basic notation and mathematical concepts for detecting and describing image features. Then, it discusses properties of perfect features and gives an overview of various existing detection and description methods. Furthermore, it explains some approaches to feature matching. Finally, the chapter discusses the most used techniques for performance evaluation of detection and description algorithms.

202 citations

Book ChapterDOI
05 Feb 2018
TL;DR: Experimental results demonstrate that the proposed shallow-water image enhancement method can achieve better perceptual quality, higher image information entropy, and less noise, compared to the state-of-the-art underwater image enhancement methods.
Abstract: Light absorption and scattering lead to underwater image showing low contrast, fuzzy, and color cast. To solve these problems presented in various shallow-water images, we propose a simple but effective shallow-water image enhancement method - relative global histogram stretching (RGHS) based on adaptive parameter acquisition. The proposed method consists of two parts: contrast correction and color correction. The contrast correction in RGB color space firstly equalizes G and B channels and then re-distributes each R-G-B channel histogram with dynamic parameters that relate to the intensity distribution of original image and wavelength attenuation of different colors under the water. The bilateral filtering is used to eliminate the effect of noise while still preserving valuable details of the shallow-water image and even enhancing local information of the image. The color correction is performed by stretching the ‘L’ component and modifying ‘a’ and ‘b’ components in CIE-Lab color space. Experimental results demonstrate that the proposed method can achieve better perceptual quality, higher image information entropy, and less noise, compared to the state-of-the-art underwater image enhancement methods.

148 citations

Journal ArticleDOI
TL;DR: The reason for underwater image degradation is presented, the state-of-the-art intelligence algorithms like deep learning methods in underwater image dehazing and restoration are surveyed, the performance of underwater images dehazed and color restoration with different methods are demonstrated, an underwater image color evaluation metric is introduced, and an overview of the major underwater image applications are provided.
Abstract: Underwater image processing is an intelligence research field that has great potential to help developers better explore the underwater environment. Underwater image processing has been used in a wide variety of fields, such as underwater microscopic detection, terrain scanning, mine detection, telecommunication cables, and autonomous underwater vehicles. However, underwater imagery suffers from strong absorption, scattering, color distortion, and noise from the artificial light sources, causing image blur, haziness, and a bluish or greenish tone. Therefore, the enhancement of underwater imagery can be divided into two methods: 1) underwater image dehazing and 2) underwater image color restoration. This paper presents the reason for underwater image degradation, surveys the state-of-the-art intelligence algorithms like deep learning methods in underwater image dehazing and restoration, demonstrates the performance of underwater image dehazing and color restoration with different methods, introduces an underwater image color evaluation metric, and provides an overview of the major underwater image applications. Finally, we summarize the application of underwater image processing.

133 citations

Journal ArticleDOI
TL;DR: Comparisons with other state-of-the-art methods demonstrate that the proposed underwater image enhancement method can achieve higher accuracy of estimated BLs, lower computation time, overall superior performance, and better information retention.
Abstract: Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazy image formation model is widely used to restore the image quality. It depends on two optical parameters: the background light (BL) and the transmission map (TM). Underwater images can also be enhanced by color and contrast correction from the perspective of image processing. In this paper, we propose an effective underwater image enhancement method for underwater images in composition of underwater image restoration and color correction. Firstly, a manually annotated background lights (MABLs) database is developed. With reference to the relationship between MABLs and the histogram distributions of various underwater images, robust statistical models of BLs estimation are provided. Next, the TM of R channel is roughly estimated based on the new underwater dark channel prior (NUDCP) via the statistic of clear and high resolution (HD) underwater images, then a scene depth map based on the underwater light attenuation prior (ULAP) and an adjusted reversed saturation map (ARSM) are applied to compensate and modify the coarse TM of R channel. Next, TMs of G-B channels are estimated based on the difference of attenuation ratios between R and G-B channels. Finally, to improve the color and contrast of the restored image with a dehazed and natural appearance, a variation of white balance is introduced as post-processing. In order to guide the priority of underwater image enhancement, sufficient evaluations are conducted to discuss the impacts of the key parameters including BL and TM, and the importance of the color correction. Comparisons with other state-of-the-art methods demonstrate that our proposed underwater image enhancement method can achieve higher accuracy of estimated BLs, lower computation time, overall superior performance, and better information retention.

127 citations