Mohd Kushairi

Bio: Mohd Kushairi is an academic researcher from Universiti Teknologi Petronas. The author has contributed to research in topics: Color image & Computational photography. The author has an hindex of 1, co-authored 1 publications receiving 43 citations.

Significance level of image enhancement techniques for underwater images

Abstract: Underwater imaging is quite a challenging in the area of photography especially for low resolution and ordinary digital camera. There are a few problems occur in underwater images such as limited range visibility, low contrast, non uniform lighting, blurring, bright artefacts, color diminished and noise. This paper concentrates on color diminished. Significant application of standard computer vision techniques to underwater imaging is required in dealing with the said problems. Both manual and auto level techniques are used to record the mean values of the stretched histogram. The objective of the paper is to identify which technique is more significant in terms of color correction. It is hoped that the finding will benefit to non divers to visualize the underwater as the real underwater world.

Underwater image quality enhancement through integrated color model with Rayleigh distribution

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.

Enhancement of low quality underwater image through integrated global and local contrast correction

Abstract: A method of integration global and local contrast stretching is proposed to increase underwater image quality.The integrated modification of RGB and HSV color model is applied in the proposed method.Resultant image outperforms the current state-of-the-arts techniques in terms of image details and noise.300 low contrast underwater images are used in the experiment. The attenuation of the light that travels through a water medium subjects underwater images to several problems. As a result of low contrast and color performance, images are unclear and lose important information. Therefore, the objects in these images can hardly be differentiated from the background. This study proposes a new method called dual-image Rayleigh-stretched contrast-limited adaptive histogram specification, which integrates global and local contrast correction. The aims of the proposed method are to increase image details and to improve the visibility of underwater images while enhancing image contrasts. The two main steps of the proposed method are contrast and color corrections; an underwater image undergoes the former before the latter. Global contrast correction generates dual-intensity images, which are then integrated to produce contrast-enhanced resultant images. Subsequently, such images are processed locally to enhance details. The color of the images is also corrected to improve saturation and brightness. Qualitative and quantitative results show that the contrast of the resultant image improves significantly. Moreover, image detail and color are adequately enhanced; thus, the proposed approach outperforms current state-of-the-art methods.

a New Color Correction Method for Underwater Imaging

Abstract: . Recovering correct or at least realistic colors of underwater scenes is a very challenging issue for imaging techniques, since illumination conditions in a refractive and turbid medium as the sea are seriously altered. The need to correct colors of underwater images or videos is an important task required in all image-based applications like 3D imaging, navigation, documentation, etc. Many imaging enhancement methods have been proposed in literature for these purposes. The advantage of these methods is that they do not require the knowledge of the medium physical parameters while some image adjustments can be performed manually (as histogram stretching) or automatically by algorithms based on some criteria as suggested from computational color constancy methods. One of the most popular criterion is based on gray-world hypothesis, which assumes that the average of the captured image should be gray. An interesting application of this assumption is performed in the Ruderman opponent color space lαβ, used in a previous work for hue correction of images captured under colored light sources, which allows to separate the luminance component of the scene from its chromatic components. In this work, we present the first proposal for color correction of underwater images by using lαβ color space. In particular, the chromatic components are changed moving their distributions around the white point (white balancing) and histogram cutoff and stretching of the luminance component is performed to improve image contrast. The experimental results demonstrate the effectiveness of this method under gray-world assumption and supposing uniform illumination of the scene. Moreover, due to its low computational cost it is suitable for real-time implementation.

A Survey on Underwater Image Enhancement Techniques

Abstract: This review paper deals with the methods to improve underwater image enhancement techniques, the processing of underwater image captured is necessary because the quality of underwater images affect and these image leads some serious problems when compared to images from a clearer environment. A lot of noise occurs due to low contrast, poor visibility conditions (absorption of natural light), non uniform lighting and little color variations, pepper noise and blur effect in the underwater images because of all these reasons number of methods are existing to cure these underwater images different filtering techniques are also available in the literature for processing and enhancement of underwater images one of them is image enhancement using median filter which enhances the image and help to estimate the depth map and improve quality by removing noise particles with the help of different techniques, and the other is RGB Color Level Stretching have used. Forward USM technique can also be used for image enhancement.