• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1046-1057
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• 2004

An image enhancement method using both homomorphic transformation and multiscale decomposition is proposed. The original image is first transformed to homomorphic domain by taking the logarithm, is then separated to multiscales. These multiscales are combined with weighting. The combined signal is exponentially transformed back into intensity domain. In homomorphic domain, the magnitude control of low frequency component make change the dynamic range, and the magnitude control of the other frequency components contribute to enhancement of the contrast. The "${\AA}$ trous" algorithm, which has a simple and efficient scheme, is used for multiscale decomposition. The performance of proposed method is verified by simulation.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.370-376
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• 2023

Aiming at the problems of low image enhancement accuracy, long enhancement time and poor image quality in the traditional robot sequence motion image enhancement methods, an adaptive enhancement method for robot sequence motion image is proposed. The feature representation of the image was obtained by Karhunen-Loeve (K-L) transformation, and the nonlinear relationship between the robot joint angle and the image feature was established. The trajectory planning was carried out in the robot joint space to generate the robot sequence motion image, and an adaptive homomorphic filter was constructed to process the noise of the robot sequence motion image. According to the noise processing results, the brightness of robot sequence motion image was enhanced by using the multi-scale Retinex algorithm. The simulation results showed that the proposed method had higher accuracy and consumed shorter time for enhancement of robot sequence motion images. The simulation results showed that the image enhancement accuracy of the proposed method could reach 100%. The proposed method has important research significance and economic value in intelligent monitoring, automatic driving, and military fields.

• Journal of the Korean Magnetics Society
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• v.14 no.6
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• pp.207-212
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• 2004

Eddy current (EC) testing methods are widely used in a variety of applications including the inspection of steam generator tubes in nuclear power plants, aircraft parts and airframes. A key factor that affects the EC signal is lift-off which means the physical distance between a sensor and a specimen in the testing. In practice, it is difficult to keep track of the actual value of the lift -off during a specific experiment, simulation or testing in the field, which is essential for accurate interpretation of the signal to be used in the following steps. Hence it is necessary to have a scheme to render the EC signal invariant to the effects of lift-off in spite of the changes in the real world. This paper describes a new method for compensating EC signals for variations in lift-off by acquiring an invariance feature using a homomorphic operator and neural network techniques. The signals from various lift-offs are transformed to obtain a zero lift-off equivalent signal that can be subsequently used for defect characterization in the next step.