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.read more
Citations
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Journal ArticleDOI
LIME: Low-Light Image Enhancement via Illumination Map Estimation
Xiaojie Guo,Yu Li,Haibin Ling +2 more
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
Jianrui Cai,Shuhang Gu,Lei Zhang +2 more
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
Collaborative Personalization of Image Enhancement
TL;DR: Methods for personalization of image enhancement are presented, which could be deployed in photo editing software and also in cloud-based image sharing services, and can suggest image enhancements more targeted to individual users than commercial tools with global auto-enhancement functionalities.
Proceedings ArticleDOI
Natural Rendering of Color Image based on Retinex
Shaohua Chen,Azeddine Beghdadi +1 more
TL;DR: Inspired by Retinex theory and histogram rescaling techniques, the proposed method tries to realize natural rendering of image with respect to the constraints listed above.
Proceedings ArticleDOI
Discrete Entropy and Relative Entropy Study on Nonlinear Clustering of Underwater and Arial Images
TL;DR: This study has the potential to apply on national defense and resource exploitation and describes the role of image segmentation via clustering, which is capable of both simplifying computation and accelerating convergence.
Proceedings ArticleDOI
A perceptual based contrast enhancement metric using AdaBoost
TL;DR: A new contrast enhancement metric (CEM) that is trained using several simple contrast measures and mean opinion scores obtained from human observations to mimic a human when selecting an image with the best contrast between two images is presented.
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