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- W4367153150 abstract "Despite of the rapid development of computer vision technology and the widely advancement of digital image processing systems used in many fields, some uncontrollable factors often exist during the process of image acquisition, resulting in various image defects. In particular, under poor illumination conditions, such as indoors, night-time, or cloudy days, the light reflected from the object surface may be weak; consequently, the image quality of such a low-light image may be seriously degraded due to colour distortions and noise. Reducing illumination by various techniques such as edges extraction for example the High Pass Filter is only good in performing one of the image restoration tasks, which is sharpening adjoining areas with slight difference in the brightness. However, in order to produce a complete restoration for a very low level of illuminance image, restoration process has to have a balance process of blurring and sharpening processes. The Low-Pass Filter is one of blurring filter methods that can be used to blur images. In order to find the best hybrid of image restoration algorithm, several combinations of blurring and sharpening processes have to be conducted. For that reason, this paper proposes a new method called the Multiple Adaptive Derivatives for Passive Image Processing (MADPIP) which is a module that performs a series of synergy algorithm combinations in restoring illuminated images by reducing illumination noise and revealing the facial outlines and facial features. In this work, the MADPIP global and local features extraction algorithms were investigated. It was concluded that the Zero Padding + Laplacian Masked and Border Replication + Low Pass Filter (ZPL + BRLPF) combination has the best score for the MSE assessment and thus best algorithm for global feature extraction. On the other hand, for local features extraction, the 2L derivative with 3 colour Logarithmic and HPF-Unsharp Masking with 8 scaling factors has produced the best result in retrieving the local features extraction." @default.
- W4367153150 created "2023-04-28" @default.
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- W4367153150 date "2023-01-01" @default.
- W4367153150 modified "2023-09-27" @default.
- W4367153150 title "Face Illumination Reduction Using MADPIP Restoration Approach to Biometric Patient Authentication System" @default.
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- W4367153150 doi "https://doi.org/10.1007/978-981-19-8406-8_10" @default.
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