Abstract
This paper introduces an adaptive image rendering using a parametric nonlinear mapping-function-based on the retinex model in a low-light source. For this study, only a luminance channel was used to estimate the reflectance component of an observed low-light image, therefore halo artifacts coming from the use of the multiple center/surround Gaussian filters were reduced. A new nonlinear mapping function that incorporates the statistics of the luminance and the estimated reflectance in the reconstruction process is proposed. In addition, a new method to determine the gain and offset of the mapping function is addressed to adaptively control the contrast ratio. Finally, the relationship between the estimated luminance and the reconstructed luminance is used to reconstruct the chrominance channels. The experimental results demonstrate that the proposed method leads to the promised subjective and objective improvements over state-of-the-art, scale-based retinex methods.
Highlights
The high performance and miniaturization of image sensors make it possible for image information to be used in various applications, such as mobile platforms, recognition systems, and security systems [1,2]
AMSR had the lowest score among the comparative methods due to the number of halo artifacts, it outperformed the others in terms of contrast per pixel (CPP)
This paper presents an adaptive image rendering method using the asymmetry of an observed image in a low-light environment
Summary
The high performance and miniaturization of image sensors make it possible for image information to be used in various applications, such as mobile platforms, recognition systems, and security systems [1,2]. A single-scale retinex (SSR) method has been introduced, in which a center/surround Gaussian performance of the deep learning approaches, based on the retinex model, it is necessary to study the filter is used to extract the reflectance from an observed image in accordance with the retinex model that reflects HVS. Law and based on the nonlinearity of human visual perception This leads to an enhancement of restoration (MSRCR) model have been presented to resolve the filter dependency problem [12]. In order to reduce the number of halo artifacts, a center/surround filter is only applied to applied the luminance channel in the YCbCr space color to estimate reflectance.
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