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

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 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

Investigation on data acquisition accuracy for long range communication using RFM LoRa

Low Power Wide Area Network (LPWAN) is a new wireless technology which is designed for low power with long-range communication. Long Range (LoRa) is one of the primary solutions of the technology. The objective of this work is to build a simple prototype device for the purpose of testing the long-range communications by means of Radio Frequency Modulation (RFM) LoRa and to investigate the accuracy of data transmitted using this technology by varying the distance between transmitter and receiver as well as data packet size. LoRa SX1276 is used for the testing purpose as it is mostly available in our country, Malaysia. For that purpose, LoRa transmitter and receiver node are integrated with small and simple microcontroller Arduino Uno and Raspberry as an interface of the communication. Consequences, data has been transmitted and received by both devices transmitter and receiver to investigate the effects related to their distance and different data packet. With the fixed value of spreading factor of 7, it could be observed, that, as the size of transmitted data is bigger, the longer the time required is and the accuracy is reduced. It could be seen that, for a small data size (200bytes), the accuracy is considerable good with the value around 90%

Vision based smart sorting machine

In this paper, a research on improved image processing method and a prototype of a vision based sorting machine have been developed to segregate objects based on color, shape and size. In today’s world, image processing is becoming popular technology and it grabs great attentions due to its capabilities of doing various applications in many field. The existing sorting system in industrial environment has to be improved by implementing the image processing method in the system. In some light industries, sorting process will be carried out by manually using human labour. However, this traditional method has brought some disadvantages such as human mistake, slow in work speed, inaccuracy and high cost due to the manpower. A vision based smart sorting machine is proposed to solve the aforementioned problems by segregating the workpieces based on their color, shape and size. It will be operated by a singleboard mini-computer called Raspberry Pi to perform the operation. In the proposed system, Raspberry Pi camera is used to capture the image/stream video of the incoming workpieces through the conveyor. The image/video stream of the incoming workpiece will be captured and implemented with pre- processing that consists of image enhancement to reduce the effect of non-uniform illumination which results from the surrounding llumination. To detect the color of the workpiece, the pre-enhanced image will be decomposed into its respective channels and the dominant color channel will be regarded as the object color. The result will be then matched with the database which is pre- installed in the raspberry storage through features matching method. The results from the features matching will turn on the servo motor and separates the workpieces’ color. For the purpose of shape segregation, the captured image will be first converted into black and white image before it is matched with the database based on certain coverage object properties. While for size segregation, the coverage object pixel area of the pre-processing image is extracted and matched with the databased in the system. Tested results indicate that vision based automatic segregation system improves the accuracy and efficiency of the works and thus the production rate of the industr

Development of controllino program to integrate the sensor inputs for motors control

The system fishcake machine available in the market cannot be controlled by its motor speed based on the desired speed. In addition, this machine uses PLC which causes the production cost of the machine to increase, cannot use components that have a small voltage such as 5V and indirectly makes the PLC a difficult component to integrate all types of components in one PLC only. So, in this paper, we will develop a program using MAXI Automation Controllino for motor speed control that can be changed according to the desired speed with PWM method. In addition, to analyse and validate the integration sensors and motors to control the entire system. Finally, in this study we use Controllino type Maxi Automation to replace the existing PLC in the market and this Controllino will relate to a sensor that is an ultrasonic sensor, infrared range sensor and a DC motor. To control this system, we use a software method that is Arduino IDE to write all the coding. This coding will be written based on all the programs or processes involved in the fishcake machine performed

Implementation of underwater image enhancement for corrosion pipeline inspection (UIECPI)

The corrosion penetration rate (CPR) during crude oil transportation procedures or gas transportation by carbon steel pipelines is one of the most important critical issue problems for any oil and gas sector today. Several studies have been conducted on these topics using various methods. The major purpose of this research is to use computer vision concepts which is underwater image enhancement for corrosion pipeline inspection to develop a robust and capable model that can accurately detect corrosion using certain algorithms and operating parameters. A reliable algorithm is developed to enhance the input images. The results from this detection model showed that, with small set of examples image, the underwater image enhancement for corrosion pipeline inspection (UIECPI) was able readily distinguished

Unsupervised Contrast Correction for Underwater Image Quality Enhancement through Integrated-Intensity Stretched-Rayleigh Histograms

The attenuation of light that travels through the water medium results the underwater image to suffer from several problems. Low contrast and color performance are the problems that resulting the image to loss important information. In addition, the objects in the image are hardly differentiated from the background. Consequences from these problems, this paper extend the methods of enhancing the quality of underwater image with the aim of improving the image contrast and increase the color performance. The proposed method consists of two stages. At first stage, contrast correction technique is applied to the image. The image is multiplied with a gain factor. The image histogram is divided into two regions at the mid-point and stretched towards the higher and lower intensity values. The composition of these two different intensities images produces contrast-enhanced image. At the second stage, the image is applied with color correction, where the image is converted into Hue-Saturation-Value (HSV) color model. Dividing and stretching of S and V components increase the image color. By considering the contrast and color performance of the output image, the proposed method outperforms the state-of-the-art method

Improving images in turbid water through enhanced color correction and particle swarm-intelligence fusion (CCPF)

When light travels through a water medium, selective attenuation and scattering have a profound impact on the underwater image. These limitations reduce image quality and impede the ability to perform visual tasks. The suggested integrated color correction with intelligence fusion of particle swarm technique (CCPF) is designed with four phases. The first phase presents a novel way to make improvement on underwater color cast. Limit the improvement to only red color channel. In the second phase, an image is then neutralized from the original image by brightness reconstruction technique resulting in enhancing the image contrast. Next, the mean adjustment based on particle swarm intelligence is implemented to improve the image detail. With the swarm intelligence method, the mean values of inferior color channels are shifted to be close to the mean value of a good color channel. Lastly, a fusion between the brightness reconstructed histogram and modified mean particle swarm intelligence histogram is applied to balance the image color. Analysis of underwater images taken in different depths shows that the proposed CCPF method improves the quality of the output image in terms of neutralizing effectiveness and details accuracy, consequently, significantly outperforming the other state-of-the-art methods. The proposed CCPF approach produces highest average entropy value of 7.823 and average UIQM value of 6.287

Enhancement of Low-Quality Diatom Images using Integrated Automatic Background Removal (IABR) Method from Digital Microscopic Image

Most diatom images scanned from digital microscopes suffer from low contrast, noise, and contain unwanted floating particles and debris in a single image. Moreover, the active movement of diatom along with poor lens focusing produces a blurred image. Thus, in this paper, we introduce a new integrated automatic background removal technique (IABR) to enhance low-quality microscopic diatom images. This paper describes a two-step process of microscopic diatom image for image smoothing. First, haze removal technique is applied to the low light image to enhance and removes the image from haze and noise. Second, the background removal technique extracts the diatom cell from the background image and improves the image contrast. The output results show that the proposed IABR method has successfully enhanced and smooths low-quality diatom images by removing the image background and improving image contrast