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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Proceedings ArticleDOI
Multi-scale retinex for color image enhancement
TL;DR: A multi-scale retinex (MSR) which overcomes this limitation for most scenes and both color rendition and dynamic range compression are successfully accomplished except for some "pathological" scenes that have very strong spectral characteristics in a single band.
Journal ArticleDOI
Blind contrast enhancement assessment by gradient ratioing at visible edges
TL;DR: In this article, an approach is proposed which consists in computing the ratio between the gradient of the visible edges between the image before and after contrast restoration, which is an indicator of visibility enhancement.
Journal ArticleDOI
Gray and color image contrast enhancement by the curvelet transform
TL;DR: A new method for contrast enhancement based on the curvelet transform is presented, which out-performs other enhancement methods on noisy images, but on noiseless or nearNoiseless images curvelet based enhancement is not remarkably better than wave let based enhancement.
Journal ArticleDOI
High dynamic range image rendering with a retinex-based adaptive filter
L. Meylan,Sabine Süsstrunk +1 more
TL;DR: The novelties of the method is first to use an adaptive filter, whose shape follows the image high-contrast edges, thus reducing halo artifacts common to other methods, and only the luminance channel is processed.
Journal ArticleDOI
Transform-based image enhancement algorithms with performance measure
TL;DR: A new class of the "frequency domain"-based signal/image enhancement algorithms including magnitude reduction, log-magnitude reduction, iterative magnitude and a log-reduction zonal magnitude technique, based on the so-called sequency ordered orthogonal transforms, which include the well-known Fourier, Hartley, cosine, and Hadamard transforms.
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