A novel Retinex image enhancement approach via brightness channel prior and change of detail prior

Gu, Zhenfei; Ju, Mingye

doi:10.1134/s1054661817020055

Cited by 12 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Table 3, our method outperforms others for five images. This evidence verifies the outstanding visibility improvement ability of our method, and this conclusion is consistent with our observation of [24]. (e) The result of TVR [18].…”

Section: Quantitative Comparisonssupporting

confidence: 91%

“…In order to verify the proposed LIDM as well as the corresponding method, we select several challenging nature low-light images and conduct the qualitative and quantitative comparison with five state-of-the-art low-light image enhancement methods, including the adaptive histogram equalization (AHE) [6], Multiscale Retinex (MSR) [15], prior-based Retinex (PBR) [24], total variation Retinex (TVR) [18], and the inverted low-light image enhancement (ILIE) [25]. The parameters in our method are all demonstrated in Section 4, and the parameters in the five comparison methods are set to be optimal according to [6,15,18,24,25] for the sake of fairness.…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, we conduct a number of experiments on various types of about 2,000 low-light images as well as the corresponding enhanced ones (obtained using several stateof-the-art methods [6,15,18,24]), respectively. Luckily, we finally notice that the "pure pixel ratio" of the low-light images and the corresponding enhanced ones, are quite different.…”

Section: Pure Pixel Ratio Priormentioning

confidence: 99%

“…However, we can also notice that the transmission jump is obvious around the scene edge and it will further lead to negative effect in the enhanced image. The abrupt transmission is mainly caused by the scene-based transmission estimation; hence, we introduce a total variation smoothing operator [24] to refine the rough transmission map as follows:…”

Section: Scene Transmission Estimationmentioning

confidence: 99%

“…Figures 14-19 demonstrate the qualitative comparison with five state of the arts. The input low-light images are displayed in column (a); and columns (b) to (h), from left to right, demonstrate the enhancement results of AHE [6], MSR [15], PBR [24], TVR [18], ILIE [25], and ours, respectively.…”

Section: Experimental Comparisons For Refinement Patch Sizementioning

confidence: 99%

See 4 more Smart Citations

A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

Chen

Zhang

2018

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Images captured in low-light conditions are prone to suffer from low visibility, which may further degrade the performance of most computational photography and computer vision applications. In this paper, we propose a low-light image degradation model derived from the atmospheric scattering model, which is simple but effective and robust. Then, we present a physically valid image prior named pure pixel ratio prior, which is a statistical regularity of extensive nature clear images. Based on the proposed model and the image prior, a corresponding low-light image enhancement method is also presented. In this method, we first segment the input image into scenes according to the brightness similarity and utilize a high-efficiency scene-based transmission estimation strategy rather than the traditional per-pixel fashion. Next, we refine the rough transmission map, by using a total variation smooth operator, and obtain the enhanced image accordingly. Experiments on a number of challenging nature low-light images verify the effectiveness and robustness of the proposed model, and the corresponding method can show its superiority over several state of the arts.

show abstract

Section: Quantitative Comparisonssupporting

confidence: 91%