A low‐light image enhancement method based on bright channel prior and maximum colour channel

Most medical images are low in contrast because adequate details that may prove vital decisions are not visible to the naked eye. Also, due to the low-contrast nature of the image, it is not easily segmented because there is no significant change between the pixel values, which makes the gradient very small Hence, the contour cannot converge on the edges of the object. In this work, we have proposed an ensembled spatial method for image enhancement. In this ensembled approach, we first employed the Laplacian filter, which highlights the areas of fast intensity variation. This filter can determine the sufficient details of an image. The Laplacian filter will also improve those features having shrill disjointedness. Then, the gradient of the image has been determined, which utilizes the surrounding pixels for the weighted convolution operation for noise diminishing. However, in the gradient filter, there is one negative integer in the weighting. The intensity value of the middle pixel might be deducted from the surrounding pixels, to enlarge the difference between the head-to-head pixels for calculating the gradients. This is one of the reasons due to which the gradient filter is not entirely optimistic, which may be calculated in eight directions. Therefore, the averaging filter has been utilized, which is an effective filter for image enhancement. This approach does not rely on the values that are completely diverse from distinctive values in the surrounding due to which it recollects the details of the image. The proposed approach significantly showed the best performance on various images collected in dynamic environments.

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“…Existing approaches Performance (%) [26] 89 [27] 91 [28] 88 [29] 93 [30] 90 [31] 94 [32] 91 Proposed approach 98…”

Section: Discussionmentioning

confidence: 99%

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

Ṣiddiqi

Alsirhani

2022

Journal of Healthcare Engineering

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“…In [30], Ren et al propose a Low-Rank Regularized Retinex Model (LR3M) via imposing the low-rank prior on the objective function, aiming to suppress noise in the reflectance map. Different from the full Retinex models, simplified Retinex-based models [4,9,34,45] estimate the illumination layer only, and treat the obtained reflection map as the final enhanced image. For example, Guo et al [9] roughly estimate the initial illumination map by extracting the maxRGB image from the color channels, and construct an edge-preserving filter to further refine the illumination map.…”

Section: Related Workmentioning

confidence: 99%

Decoupled Low-light Image Enhancement

Hao¹,

Han²,

Guo³

et al. 2021

Preprint

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The visual quality of photographs taken under imperfect lightness conditions can be degenerated by multiple factors, e.g., low lightness, imaging noise, color distortion and so on. Current low-light image enhancement models focus on the improvement of low lightness only, or simply deal with all the degeneration factors as a whole, therefore leading to a sub-optimal performance. In this paper, we propose to decouple the enhancement model into two sequential stages. The first stage focuses on improving the scene visibility based on a pixel-wise non-linear mapping. The second stage focuses on improving the appearance fidelity by suppressing the rest degeneration factors. The decoupled model facilitates the enhancement in two aspects. On the one hand, the whole low-light enhancement can be divided into two easier subtasks. The first one only aims to enhance the visibility. It also helps to bridge the large intensity gap between the low-light and normal-light images. In this way, the second subtask can be shaped as the local appearance adjustment. On the other hand, since the parameter matrix learned from the first stage is aware of the lightness distribution and the scene structure, it can be incorporated into the second stage as the complementary information. In the experiments, our model demonstrates the state-of-the-art performance in both qualitative and quantitative comparisons, compared with other low-light image enhancement models. In addition, the ablation studies also validate the effectiveness of our model in multiple aspects, such as model structure and loss function. The trained model is available at https://github.com/hanxuhfut/Decoupled-Low-light-Image-Enhancement.

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“…Therefore, to overcome these mentioned problems, an initial illumination map estimation algorithm is proposed to provide near actual estimation of the illumination map of low-light images. The initial illumination of low-light image in the proposed algorithm is obtained based on the fusion of both the maximum color and bright channels to reduce the color distortion and the halo artifacts problems produced from both channels [39]. First, Gaussian filter [40] is applied to the bright channel (𝑳 ̂𝑏𝑟𝑖𝑔ℎ𝑡 ) to smoothen its halo artifacts.…”

Section: Initial Illumination Map Estimationmentioning

confidence: 99%

SPLIE: Optimal Illumination Estimation for Structure Preserving Low-light Image Enhancement

Sandoub

Atta

Abdel-Kader

et al. 2021

Port-Said Engineering Research Journal

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The images taken in low-light conditions often have many flaws such as, color vividness and low visibility which negatively affects the performance of many vision-based systems. Many of the existing Retinex-based enhancement algorithms improve the visibility of low-light images via estimating the illumination map and use it to obtain the corresponding reflectance. However, the improper estimation of the initial illumination map may produce unsatisfactory illuminated enhanced images with weak color constancy. To address this problem, this paper proposes an efficient algorithm for the enhancement of low-light images. In this algorithm, the initial illumination map is obtained by the fusion between the maximum color channel and bright channel prior. The estimated initial illumination map is then refined using a multi-objective problem that contains the illumination regularization terms specifically, the structural and textural details of the illumination. The optimization problem is solved using the alternative direction minimization (ADM) technique with the augmented Lagrangian multiplier to produce structure-aware smoothness of the initial illumination map. Finally, the contrast of the refined illumination map is adjusted using the gamma correction method. Experimental results on several benchmark datasets reveal the superiority of the proposed algorithm on the state-of-theart algorithms in terms of qualitative and quantitative analysis. Furthermore, the proposed algorithm produces enhanced images with reducing the artifacts and preserving the naturalness and structural details.

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A low‐light image enhancement method based on bright channel prior and maximum colour channel

Cited by 19 publications

References 47 publications

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

Decoupled Low-light Image Enhancement

SPLIE: Optimal Illumination Estimation for Structure Preserving Low-light Image Enhancement

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