Abstract
This paper proposes the development and evaluation of an auto-tuning stochastic resonance (SR) for image enhancement on images under various illumination conditions. Perceptual Quality Metric (PQM) is used for evaluating and quantifying the image quality. The current process is developed being based on our previous works related to the image enhancement by using the manual tuning stochastic resonance. The process was performed by adding the random noise and threshold in an image. The process works properly in the dark and very low contrast images as well as bright images. This image enhancement system works on dark and bright images as well. The system was tested with the face detection algorithm on the dark and illumination variant images. In this paper, we present the idea of auto-tuning of the SR iteration with random noise and threshold value 0 by using the process related to the histogram calculation, mean and median. In this paper, we performed various experiments on object and human detection as well under different conditions and confirmed the effectiveness of our auto-tuning SR based image enhancement algorithm. Finally, we conducted experiments using Perceptual Quality Metric which is an image quality metric to apply the proposed algorithm in various fields.
Highlights
In an image obtained from a consumer based digital camera, it is very difficult to discriminate an object to be detected from an image due to the several shooting situations under various illumination conditions such as lightening, back lightening, darkness, brightness, halation, flare, ghost, etc
There exists a lot of case images where the image cannot be enhanced by the contrast adjustment process, which has been clarified in our previous researches related to the face and human body detection on dark and illumination variant images [1]∼[3]
The proposed method improves excessive detection and insufficient detection compared with the contrast adjustment method
Summary
In an image obtained from a consumer based digital camera, it is very difficult to discriminate an object to be detected from an image due to the several shooting situations under various illumination conditions such as lightening, back lightening, darkness, brightness, halation, flare, ghost, etc. There exists a lot of case images where the image cannot be enhanced by the contrast adjustment process, which has been clarified in our previous researches related to the face and human body detection on dark and illumination variant images [1]∼[3] The another process for image enhancement on such type of images is image sharpening and filtering, that enhances high frequency components of edges and details for optically degraded images. We are proposing an image enhancement method that auto tunes the SR parameters in order to solve the difficult problem of feature detection and recognition due to the influence of contrast such as illumination variant, dark and bright image. We conducted experiments using Perceptual Quality Metric (PQM) which is an image quality metric as an evaluation method to apply the proposed method in various fields
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