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Journal ArticleDOI

Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images

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TLDR
Experimental results demonstrate that the proposed enhancement algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.
Abstract
Image enhancement plays an important role in image processing and analysis. Among various enhancement algorithms, Retinex-based algorithms can efficiently enhance details and have been widely adopted. Since Retinex-based algorithms regard illumination removal as a default preference and fail to limit the range of reflectance, the naturalness of non-uniform illumination images cannot be effectively preserved. However, naturalness is essential for image enhancement to achieve pleasing perceptual quality. In order to preserve naturalness while enhancing details, we propose an enhancement algorithm for non-uniform illumination images. In general, this paper makes the following three major contributions. First, a lightness-order-error measure is proposed to access naturalness preservation objectively. Second, a bright-pass filter is proposed to decompose an image into reflectance and illumination, which, respectively, determine the details and the naturalness of the image. Third, we propose a bi-log transformation, which is utilized to map the illumination to make a balance between details and naturalness. Experimental results demonstrate that the proposed algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.

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Citations
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Book ChapterDOI

Night Time Image Enhancement by Improved Nonlinear Model

TL;DR: An adaptive night time image improving method in the basis of nonlinear brightness enhancement model is proposed to process the segmentations of image brightness by using the logarithmic function.
Journal ArticleDOI

Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework

TL;DR: A plug-and- play framework based on the Retinex theory for simultaneously image enhancement and noise removal and a convolutional neural network-based (CNN-based) denoiser is developed into this framework to generate a reflectance component.
Journal ArticleDOI

Brightness and Contrast Enhancement Method for Color Images Via Pairing Adaptive Gamma Correction and Histogram Equalization

TL;DR: In this article , an adaptive gamma generator is proposed to adaptively calculate gamma parameters in accordance with dark, medium, or bright image conditions, and the computed gamma parameters are used to propose a cumulative distribution function that produces an optimized curve for illumination values.
Journal ArticleDOI

Effective method for low-light image enhancement based on the JND and OCTM models.

TL;DR: Zhang et al. as mentioned in this paper proposed an effective method for low-light image enhancement based on the just-noticeable-difference (JND) and the optimal contrast-tone mapping (OCTM) models.
Journal ArticleDOI

A Structure Preservation and Denoising Low-Light Enhancement Model via Coefficient of Variation

TL;DR: SPDLEM as mentioned in this paper uses the coefficient of variation to process the original low-light image, which is used to obtain the enhanced illumination gradient reference map, and then combines the above two constraint terms with the Retinex theory, which contains the denoising regular term.
References
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Journal ArticleDOI

Lightness and Retinex Theory

TL;DR: The mathematics of a lightness scheme that generates lightness numbers, the biologic correlate of reflectance, independent of the flux from objects is described.
Journal ArticleDOI

A multiscale retinex for bridging the gap between color images and the human observation of scenes

TL;DR: This paper extends a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition and defines a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency.
Book

Handbook of Image and Video Processing

Alan C. Bovik
TL;DR: The Handbook of Image and Video Processing contains a comprehensive and highly accessible presentation of all essential mathematics, techniques, and algorithms for every type of image and video processing used by scientists and engineers.
Journal ArticleDOI

Properties and performance of a center/surround retinex

TL;DR: A practical implementation of the retinex is defined without particular concern for its validity as a model for human lightness and color perception, and the trade-off between rendition and dynamic range compression that is governed by the surround space constant is described.
Journal ArticleDOI

Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement

TL;DR: A system level realization of CLAHE is proposed, which is suitable for VLSI or FPGA implementation and the goal for this realization is to minimize the latency without sacrificing precision.
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