An edge-directed bicubic interpolation algorithm

Zhou, Dengwen

doi:10.1109/cisp.2010.5647190

Cited by 47 publications

(10 citation statements)

References 16 publications

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“…Bicubic interpolation is one method of image resizing, where the output pixel value is weighted average calculated over a

neighbourhood surrounding the input pixel. This method produces a smooth image compared to other interpolation methods and is popular in many image processing algorithms [ 34 ].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Imaging Time Series for the Classification of EMI Discharge Sources

Mitiche

Morison

Nesbitt

et al. 2018

Sensors

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In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome.

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“…Bicubic interpolation is one method of image resizing, where the output pixel value is weighted average calculated over a

neighbourhood surrounding the input pixel. This method produces a smooth image compared to other interpolation methods and is popular in many image processing algorithms [ 34 ].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Imaging Time Series for the Classification of EMI Discharge Sources

Mitiche

Morison

Nesbitt

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…In recent years, as the most direct way to obtain information, images are used in important applications such as facial recognition, medical imaging, video monitoring, remote sensing imaging, computer vision and other fields. In the current research field, the methods for image superresolution mainly include interpolation [1]- [5], reconstruction constraint [6], [7] and learning [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Underwater Image High Definition Display Using the Multilayer Perceptron and Color Feature-Based SRCNN

Zhang

et al. 2019

IEEE Access

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High-definition display technology for underwater images is of great significance for many applications, such as marine animal observation, seabed mining, and marine fishery production. The traditional underwater visual display systems have problems, such as low visibility, poor real-time performance, and low resolution, and cannot meet the needs of real-time high-definition displays in extreme environments. To solve these issues, we propose an underwater image enhancement method and a corresponding image super-resolution algorithm. To improve the quality of underwater images, we modify the Retinex algorithm and combine it with a neural network. The Retinex algorithm is used to defog the underwater image, and then, the image brightness is improved by applying gamma correction. Then, by combining with the dark channel prior and multilayer perceptron, the transmission map is further refined to improve the dynamic range of the image. In the super-resolution process, the current convolutional neural network reconstruction algorithm is only trained on the Y channel, which will lead to problems due to the insufficient acquisition of the color feature. Therefore, an image super-resolution reconstruction algorithm that is based on color features is proposed. The experimental results show that the proposed method improves the reconstruction effect of the image edges and texture details, increases the image clarity, and enhances the image color recovery. INDEX TERMS Image enhancement, superresolution, convolutional neural networks, underwater imaging.

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“…However, it also produces certain undesirable results when some textures details need to be preserved. In order to compensate for this shortcoming, Dengwen [4] proposed a new algorithm based on Bicubic Convolution (BC) Interpolation, which can produce sharp edges as well as texture details. Other than these methods, super-resolution algorithm [5][6][7] has been widely investigated in image processing field as well.…”

Section: Introductionmentioning

confidence: 99%

Resolution Enhancement Method Used for Force Sensing Resistor Array

et al. 2015

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Tactile sensors are one of the major devices that enable robotic systems to interact with the surrounding environment. This research aims to propose a mathematical model to describe the behavior of a tactile sensor based on experimental and statistical analyses and moreover to develop a versatile algorithm that can be applied to different tactile sensor arrays to enhance the limited resolution. With the proposed algorithm, the resolution can be increased up to twenty times if multiple measurements are available. To verify if the proposed algorithm can be used for tactile sensor arrays that are used in robotic system, a 16 × 10 force sensing array (FSR) is adopted. The acquired two-dimensional measurements were processed by a resolution enhancement method (REM) to enhance the resolution, which can be used to improve the resolution for single image or multiple measurements. As a result, the resolution of the sensor is increased and it can be used as synthetic skin to identify accurate shapes of objects and applied forces.

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An edge-directed bicubic interpolation algorithm

Cited by 47 publications

References 16 publications

Imaging Time Series for the Classification of EMI Discharge Sources

Imaging Time Series for the Classification of EMI Discharge Sources

Underwater Image High Definition Display Using the Multilayer Perceptron and Color Feature-Based SRCNN

Resolution Enhancement Method Used for Force Sensing Resistor Array

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