Automatic System for Improving Underwater Image Contrast and Color Through Recursive Adaptive Histogram Modification

Contrast and color are important attributes to extract and acquire much information from underwater images. However, normal underwater images contain bright foreground and dark background areas. Previous enhancement methods enhance the foreground areas but retain darkness and blue-green illumination of background areas. This study proposes a new method of enhancing underwater image, which is called recursive adaptive histogram modification (RAHIM), to modify image histograms column wisely in accordance with Rayleigh distribution. Modifying image saturation and brightness in the hue–saturation–value color model increases the natural impression of image color through the human visual system. Qualitative and quantitative evaluations prove the effectiveness of the proposed method. Comparison with state-of-the-art methods shows that the proposed method produces the highest average entropy, measure of enhancement (EME), and EME by entropy with the values of 7.618, 28.193, and 6.829, respectively

Improvement of Underwater Image Contrast Enhancement Technique Based on Histogram Modification

Degradasi kontras adalah salah satu masalah imej bawah air yang mengakibatkan pengurangan keamatan cahaya. Kontras yang rendah menyumbang kepada masalah imej yang mempunyai kurang maklumat. Objek dalam imej dilihat tidak jelas. Tambahan juga, penyerapan cahaya menyebabkan imej yang diambil kelihatan berwarna biru-kehijauan seterusnya warna objek akan disalah tafsir. Selain itu, kewujudan kawasan yang gelap dan terlalu cerah menyebabkan pengurangan keperincian imej. Oleh itu, untuk mengurangkan masalah yang dinyatakan di atas, tiga teknik untuk meningkatkan kontras imej di bawah air telah dicadangkan dalam kajian ini, iaitu model warna bersepadu dengan pengagihan Rayleigh (ICM-RD), Rayleigh-regangan dan purata paksi imej (RSAIP), dan regangan- Rayleigh dua imej spesifikasi histogram penyesuaian terhad (DIRS-CLAHS). ICM-RD meningkatkan kontras imej di bawah air dengan mengintegrasikan pengagihan Rayleigh dalam proses regangan yang terhad. Seterusnya, pembetulan warna imej melalui model warna Hue-Ketepuan-Nilai (HSV) memperbaiki keseluruhan warna imej. Di samping itu, kaedah RSAIP dicadangkan bagi menyelesaikan masalah had regangan bagi proses regangan yang dihadapi oleh kaedah ICM-RD. Kaedah RSAIP menyediakan satu alternatif baharu bagi proses regangan, yang mana imej histogram akan dibahagi kepada dua bahagian dan diregangkan secara berasingan bagi memenuhi ruang dinamik imej yang ditetapkan. Proses pembahagian dan regangan ini menghasilkan dua imej yang berbeza keamatan. Kedua-dua imej yang dihasilkan akan digabungkan berdasarkan nilai purata dan diaplikasikan dengan kaedah pembetulan warna bagi menghasilkan imej akhir. Kaedah yang ketiga, DIRSCLAHS, dicadangkan bagi meningkatkan keupayaan kaedah RSAIP dalam mempertingkatkan kontras imej dengan mengintegrasikan pembetulan kontras global dan tempatan. Proses DIRS-CLAHS bermula dengan pembetulan kontras global yang diperkenalkan dalam kaedah RSAIP. Pembetulan kontras tempatan dilaksanakan dengan membahagikan imej kepada bahagian yang lebih kecil. Akhirnya, proses ini diaplikasikan dengan proses pembetulan warna yang merupakan modifikasi daripada proses pembetulan warna yang diperkenalkan dalam kaedah RSAIP dan ICM-RD. Secara prinsipnya, semua teknik yang dicadangkan mengatasi kualiti teknik terbaharu yang diperkenalkan secara kualiti dan kuantiti. Daripada tiga teknik yang dicadangkan, kaedah DIRS-CLAHS menunjukkan satu peningkatan yang baik dalam meningkatkan kontras imej bawah air dan warnanya. Secara kuantiti, perbandingan dengan enam teknik terbaharu yang diperkenalkan bagi 300 sampel imej, kaedah DIRS-CLAHS menghasilkan nilai purata entropi yang tertinggi iaitu 7.624 dan nilai purata MSE yang terendah iaitu 646.32. Malah, dari segi pengukuran peningkatan (EME) dan pengukuran peningkatan berdasarkan entropi (EMEE), DIRSCLAHS menghasilkan nilai purata tertinggi iaitu masing-masing 27.096 dan 9.670. ________________________________________________________________________________________________________________________ Contrast degradation is one of the problems of underwater image that resulted from the light attenuation. Low contrast contributes towards the less usable image where less information could be extracted from the image. The objects seen in the image are unclear. In addition, light absorption phenomenon causes the underwater image to be dominant by the blue-green illumination, resulting in misinterpretation of objects color. Therefore, to reduce the aforementioned problems of underwater image and increases underwater image contrast, three techniques of improving underwater image contrast are proposed in this study, namely integrated color model with Rayleigh distribution (ICM-RD), Rayleigh-stretching and averaging image planes (RSAIP), and dual-images Rayleigh-stretched contrast limited adaptive histogram specification (DIRS-CLAHS). ICM-RD improves the underwater image contrast by integrating the Rayleigh distribution in the limited stretching process. The correction of image color through Hue-Saturation-Value (HSV) color model further improves the overall image color. On the other hand, RSAIP method solves the limitation of stretching process that faced by ICM-RD method. The RSAIP method provides an alternative stretching technique, where the histogram of the original image is divided into two independent regions and stretched independently to occupy the limited dynamic intensity range. The dividing and stretching processes produce two different intensity images. These images are then combined by means of average value and applied with color correction technique to produce final resultant image. The third proposed method, DIRS-CLAHS method is designed to improve the capability of the RSAIP method in enhancing image contrast by integrating global and local contrast correction. DIRS-CLAHS is first applied with global contrast correction which is introduced in the RSAIP method. Local contrast correction is then applied by dividing the image into smaller tiles. Finally, the method is applied with a new color correction process which is a modification of color correction process introduced in RSAIP and ICM-RD methods. All proposed techniques, principally outperform the state-of-the-art methods, qualitative and quantitatively. Out of the three proposed methods, DIRS-CLAHS method, is the best method and demonstrates a significant enhancement in improving the underwater image contrast and its color. Quantitatively, in comparison with six state-of-the-art methods for 300 samples of underwater images, the proposed DIRS-CLAHS produces the highest average entropy of 7.624 and the lowest average MSE value of 646.32. In addition, in terms of measure of enhancement (EME) and measure of enhancement by entropy (EMEE), DIRSCLAHS produces the highest average values which are 27.096 and 9.670, respectively

Improvement of Auto-Tracking Mobile Robot based on HSI Color Model

Auto tracking mobile robot is a device that able to detect and track a target. For an auto tracking device, the most crucial part of the system is the object identification and tracking of the moving targets. In order to improve the accuracy of identification of object in different illumination and background conditions, the implementation of HSI color model is used in image processing algorithm. In this project HSI-based color enhancement algorithm were used for object identification. This is because HSI parameter are more stable in different light and background conditions, so it is selected as the main parameters of this system. Pixy CMUcam5 is used as the vision sensor while Arduino Uno as the main microcontroller that controls all the input and output of the device. Moreover, two servo motors were used to control the pan-tilt movement of the vision sensor. Experimental results demonstrate that when HSI color-based filtering algorithm is applied to visual tracking it improves the accuracy and stability of tracking under the condition of varying brightness, or even in the low-light-level environment. Besides that, this algorithm also prevents tracking loss due to object color appears in the background

An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging

Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. Firstly, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analysis (the used code is freely available at https://github.com/wangyanckxx/Single-Underwater-Image-Enhancement-and-Color-Restoration). Starting from this study, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field

Multimodal enhancement-fusion technique for natural images.

Masters Degree. University of KwaZulu-Natal, Durban.This dissertation presents a multimodal enhancement-fusion (MEF) technique for natural images. The MEF is expected to contribute value to machine vision applications and personal image collections for the human user. Image enhancement techniques and the metrics that are used to assess their performance are prolific, and each is usually optimised for a specific objective. The MEF proposes a framework that adaptively fuses multiple enhancement objectives into a seamless pipeline. Given a segmented input image and a set of enhancement methods, the MEF applies all the enhancers to the image in parallel. The most appropriate enhancement in each image segment is identified, and finally, the differentially enhanced segments are seamlessly fused. To begin with, this dissertation studies targeted contrast enhancement methods and performance metrics that can be utilised in the proposed MEF. It addresses a selection of objective assessment metrics for contrast-enhanced images and determines their relationship with the subjective assessment of human visual systems. This is to identify which objective metrics best approximate human assessment and may therefore be used as an effective replacement for tedious human assessment surveys. A subsequent human visual assessment survey is conducted on the same dataset to ascertain image quality as perceived by a human observer. The interrelated concepts of naturalness and detail were found to be key motivators of human visual assessment. Findings show that when assessing the quality or accuracy of these methods, no single quantitative metric correlates well with human perception of naturalness and detail, however, a combination of two or more metrics may be used to approximate the complex human visual response. Thereafter, this dissertation proposes the multimodal enhancer that adaptively selects the optimal enhancer for each image segment. MEF focusses on improving chromatic irregularities such as poor contrast distribution. It deploys a concurrent enhancement pathway that subjects an image to multiple image enhancers in parallel, followed by a fusion algorithm that creates a composite image that combines the strengths of each enhancement path. The study develops a framework for parallel image enhancement, followed by parallel image assessment and selection, leading to final merging of selected regions from the enhanced set. The output combines desirable attributes from each enhancement pathway to produce a result that is superior to each path taken alone. The study showed that the proposed MEF technique performs well for most image types. MEF is subjectively favourable to a human panel and achieves better performance for objective image quality assessment compared to other enhancement methods

Automatic phytoplankton image smoothing through integrated dual image histogram specification and enhanced background removal method

Diatom is a dominant phytoplankton and commonly found in oceans or waterways. The captured phytoplankton microscopic images suffer from low contrast and surrounding debris. These images are not appropriated for identification. Integrated dual image contrast adaptive histogram specification with enhanced background removal (DIHS-BR) is proposed to address these issues by automatically removes the background of the phytoplankton image and improves the image quality while cropping phytoplankton cell. DIHS-BR will automatically remove the background and noises. DIHS-BR consists of two major steps, namely, contrast adaptive histogram specification and background removal by means of edge mask cropping. Results demonstrated that DIHS-BR filtered out the image background and left only the required phytoplankton cell image. Noises are minimized, while the contrast and colour of phytoplankton cells are improved. The average edge-based contrast measure (EBCM) of 83.065 demonstrates the best contrast improvement of the proposed methods compared with the other state-of-the-art methods

Mitigation of contrast loss in underwater images

The quality of an underwater image is degraded due to the effects of light scattering in water, which are resolution loss and contrast loss. Contrast loss is the main degradation problem in underwater images which is caused by the effect of optical back-scatter. A method is proposed to improve the contrast of an underwater image by mitigating the effect of optical back-scatter after image acquisition. The proposed method is based on the inverse model of an underwater image model, which is validated experimentally in this work. It suggests that the recovered image can be obtained by subtracting the intensity value due to the effect of optical back-scatter from the degraded image pixel and then scaling the remaining by a factor due to the effect of optical extinction. Three filters are proposed to estimate for optical back-scatter in a degraded image. Among these three filters, the performance of BS-CostFunc filter is the best. The physical model of the optical extinction indicates that the optical extinction can be calculated by knowing the level of optical back-scatter. Results from simulations with synthetic images and experiments with real constrained images in monochrome indicate that the maximum optical back-scatter estimation error is less than 5%. The proposed algorithm can significantly improve the contrast of a monochrome underwater image. Results of colour simulations with synthetic colour images and experiments with real constrained colour images indicate that the proposed method is applicable to colour images with colour fidelity. However, for colour images in wide spectral bands, such as RGB, the colour of the improved images is similar to the colour of that of the reference images. Yet, the improved images are darker than the reference images in terms of intensity. The darkness of the improved images is because of the effect of noise on the level of estimation errors.EThOS - Electronic Theses Online Servicety of ManchesterThe Petroleum Institute in Abu DhabiGBUnited Kingdo

Domain-inspired image processing and computer vision to support deep-sea benthic ecology

Optical imagery is a necessary methodological tool for ecological research within marine environments, particularly in deeper waters. For benthic (seafloor) surveys, interpretation of image data is crucial to creating high-resolution maps of seabed habitats. This is fundamental to marine spatial planning and mitigating long-term damage of anthropogenic stressors such as growing resource demand, climate change and pollution. However there are numerous, and significant, issues in extracting a reliable ground-truth from imagery to support this process. Analysis of benthic images is difficult, due in part to the extreme variation and inconsistency in image quality - caused by complex interactions between light and water. It is also time-consuming. This thesis is dedicated to providing solutions to manage these challenges, from a strong perspective of the end-user. Specifically, we aim to improve the annotation of benthic habitats from imagery in terms of quality, consistency and efficiency. Throughout, we consider the purpose the imagery serves and work closely with end-users to best optimize our solutions. First, and for the majority of this thesis, we investigate image processing techniques to improve the appearance of image features important for habitat classification. We find that tone mapping is an effective and simple (and thus accessible) method through which to improve image quality for interpretation. We describe beneficial (expert-informed) properties for brightness distributions in underwater images and introduce a novel tone-mapping algorithm, Weibull Tone Mapping (WTM), to enhance benthic images. WTM theory operates within general constraints that model image requirements (properties) specified by image analysts, yet possesses a suitable degree of flexibility and customisation. As a tool, WTM provides analysts with a fast and ‘user-friendly’ method to improve benthic habitat classification. Second, we consider computer vision methods that could automatically identify benthic habitats in imagery, relieving the analysis bottleneck. We find that baseline transfer learning of machine learning models, with limited optimization, will better facilitate adoption by novice users, yet still provides a powerful means to swiftly extract and assess benthic data