A fusion-based enhancing method for weakly illuminated images

Fu, Xueyang; Zeng, Delu; Huang, Yue; Liao, Yinghao; Ding, Xinghao; Paisley, John

doi:10.1016/j.sigpro.2016.05.031

Cited by 581 publications

(419 citation statements)

References 45 publications

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“…As can be seen, on all the six cases, STAR achieves visually clear content while enhancing the illumination naturally, in agreement with our objective results. Besides, from the second row of Figure 9, one can observe that the proposed STAR also performs better than the competing methods [6]- [8], [50] on noise suppression.…”

Section: A Low-light Image Enhancementmentioning

confidence: 89%

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STAR: A Structure and Texture Aware Retinex Model

Hou

Ren

et al. 2020

IEEE Trans. on Image Process.

202

114

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Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives are attributed to the changes in reflectance, while smaller derivatives are emerged in the smooth illumination. In this paper, we propose to utilize the exponentiated derivatives (with an exponent γ) of an observed image to generate a structure map when being amplified with γ > 1 and a texture map when being shrank with γ < 1. To this end, we design exponential filters for the local derivatives, and present their capability on extracting accurate structure and texture maps, influenced by the choices of exponents γ on the local derivatives. The extracted structure and texture maps are employed to regularize the illumination and reflectance components in Retinex decomposition. A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image. We solve the STAR model in an alternating minimization manner. Each sub-problem is transformed into a vectorized least squares regression with closed-form solution. Comprehensive experiments demonstrate that, the proposed STAR model produce better quantitative and qualitative performance than previous competing methods, on illumination and reflectance estimation, low-light image enhancement, and color correction.

show abstract

Section: A Low-light Image Enhancementmentioning

confidence: 89%

“…We compare the proposed STAR model on the two sets of images previously mentioned. From [8], [32], [50] and 200 low-light images provided in [5].…”

Section: A Low-light Image Enhancementmentioning

confidence: 99%

STAR: A Structure and Texture Aware Retinex Model

Hou

Ren

et al. 2020

IEEE Trans. on Image Process.

202

114

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“…Jobson et al [23] made an early attempt to this problem, but their results often look unnatural due to the frequently appeared artifacts such as loss of details, color distortion, and uneven exposure. Subsequent methods in this category focus on improving the results [1], [24], [2], [3], [4]. However, they may also fail, especially for nonuniformly illuminated underexposed images.…”

Section: Histogram-based Methodsmentioning

confidence: 99%

Enhancing Underexposed Photos Using Perceptually Bidirectional Similarity

Zhang

Nie

Zhu

et al. 2021

IEEE Trans. Multimedia

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This paper addresses the problem of enhancing underexposed photos. Existing methods have tackled this problem from many different perspectives and achieved remarkable progress. However, they may fail to produce satisfactory results due to the presence of visual artifacts such as color distortion, loss of details and uneven exposure, etc. To obtain high-quality results free of these artifacts, we present a novel underexposed photo enhancement approach in this paper. Our main observation is that, the reason why existing methods induce the artifacts is because they break a perceptual consistency between the input and the enhanced output. Based on this observation, an effective criterion, called perceptually bidirectional similarity (PBS) is proposed for preserving the perceptual consistency during enhancement. Particularly, we cast the underexposed photo enhancement as PBS-constrained illumination estimation optimization, where the PBS is defined as three constraints for estimating the illumination that can recover the enhancement results with normal exposure, distinct contrast, clear details and vivid color. To make our method more efficient and scalable to high-resolution images, we introduce a sampling-based strategy for accelerating the illumination estimation. Moreover, we extend our method to handle underexposed videos. Qualitative and quantitative comparisons as well as the user study demonstrate the superiority of our method over the state-of-the-art methods.

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“…In addition, there are also some black pixels in the fish area and some fish area is split. So we conduct a closed operation (corrosion after expansion) and then a median filter on the binary image [13]. And finally, a very ideal binary image is shown in figure 4.…”

Section: Otsu Methods and Related Derivativesmentioning

confidence: 99%

An Automated Fish Counting Algorithm in Aquaculture Based on Image Processing

Le¹,

Xu²

2017

Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

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Abstract.A new algorithm based on endpoints of skeleton is presented to efficiently get the number of fish in this paper. Considering the complexity of underwater environment like lack of light, this paper presents an improved adaptive thresholding method to segment the fish image better. In addition, the object of our research is free-swimming fish. The overlapped fish in the image makes the counting result inaccurate often. So after segmentation and morphological processing, this paper adopts image thinning method to extract the skeleton of fish. After that, we get the fish number according to the number of corresponding endpoints in the image. The experimental results show that the method can accurately count the fish population even under high overlapped degree.

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A fusion-based enhancing method for weakly illuminated images

Cited by 581 publications

References 45 publications

STAR: A Structure and Texture Aware Retinex Model

STAR: A Structure and Texture Aware Retinex Model

Enhancing Underexposed Photos Using Perceptually Bidirectional Similarity

An Automated Fish Counting Algorithm in Aquaculture Based on Image Processing

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