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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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Journal ArticleDOI

LIME: Low-Light Image Enhancement via Illumination Map Estimation

TL;DR: Experiments on a number of challenging low-light images are present to reveal the efficacy of the proposed LIME and show its superiority over several state-of-the-arts in terms of enhancement quality and efficiency.
Proceedings ArticleDOI

A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation

TL;DR: It is shown that, though it is widely adopted for ease of modeling, the log-transformed image for this task is not ideal and the proposed weighted variational model can suppress noise to some extent.
Journal ArticleDOI

Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images

TL;DR: This paper proposes to use the convolutional neural network (CNN) to train a SICE enhancer, and builds a large-scale multi-exposure image data set, which contains 589 elaborately selected high-resolution multi-Exposure sequences with 4,413 images.
Posted Content

Deep Retinex Decomposition for Low-Light Enhancement

TL;DR: Zhang et al. as mentioned in this paper proposed a deep Retinex-Net for low-light image enhancement, which consists of a decomposition network for decomposition and an enhancement network for illumination adjustment.
Journal ArticleDOI

Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model

TL;DR: The robust Retinex model is proposed, which additionally considers a noise map compared with the conventional RetineX model, to improve the performance of enhancing low-light images accompanied by intensive noise.
References
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Journal ArticleDOI

A Dynamic Histogram Equalization for Image Contrast Enhancement

TL;DR: This dynamic histogram equalization (DHE) technique takes control over the effect of traditional HE so that it performs the enhancement of an image without making any loss of details in it.
Journal ArticleDOI

A Histogram Modification Framework and Its Application for Image Contrast Enhancement

TL;DR: A general framework based on histogram equalization for image contrast enhancement, and a low-complexity algorithm for contrast enhancement is presented, and its performance is demonstrated against a recently proposed method.
Journal ArticleDOI

Image enhancement via adaptive unsharp masking

TL;DR: A new method for unsharp masking for contrast enhancement of images is presented that employs an adaptive filter that controls the contribution of the sharpening path in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas.
Journal ArticleDOI

Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement

TL;DR: This paper proposes a new method, known as brightness preserving dynamic histogram equalization (BPDHE), which is an extension to HE that can produce the output image with the meanintensity almost equal to the mean intensity of the input, thus fulfill the requirement of maintaining the mean brightness of the image.
Journal ArticleDOI

Statistical evaluation of image quality measures

TL;DR: It was found that measures based on the phase spectrum, the multireso- lution distance or the HVS filtered mean square error are computa- tionally simple and are more responsive to coding artifacts.
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