Nighttime image enhancement using a new illumination boost algorithm

Al-Ameen, Zohair

doi:10.1049/iet-ipr.2018.6585

Cited by 56 publications

(44 citation statements)

References 20 publications

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“…Figure 12 shows the qualitative performance, and Table 5 presents the quantitative scores of our model and its comparison with other state-of-the-art approaches. Furthermore, it has become apparent from the Figure 12, the methods such as LIIE [35], NIE [57], APNN [43], RIES [32] and FBE [36] enhanced more contents as compared to HMCE [58] and ICCE [59]. However, they not only unable to achieve the naturalness, but their improved results look hazy and lack of saturation color.…”

Section: B Results and Discussionmentioning

confidence: 99%

Efficient Image Enhancement Model for Correcting Uneven Illumination Images

Rahman

Aamir

et al. 2020

IEEE Access

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Images captured under varying light conditions have deficient contrast, low brightness, latent colors, and high noise. Numerous methods have been developed for image enhancement. However, these methods are only suitable for enhancing specific type of images (e.g., overexposed or underexposed), and also fail to restore artifact-free results for various other types of images. Therefore, to meet this goal, in this paper, we present an automatic image enhancement method, which is capable of producing quality results for all types of images captured under uneven exposure conditions (e.g., backlit, non-uniform, overexposed , one-sided illumination and night-time images). Firstly, images are categorized using a convolutional neural network (CNN) to determine their class, and different values of weight coefficients are achieved for further processing. Then, images are converted into photonegative form to obtain an initial transmission map using a bright channel prior. Next, L1-norm regularization is adopted to refine scene transmission. Besides, environmental light is estimated based on an effective filter. Finally, the image degradation model is applied to achieve enhanced results. Furthermore, post-processing of the images is comprised of two steps, such as denoising and details enhancement. The denoised model is only applied when the images are captured in extreme low-light conditions. Whereas, a smooth layer is obtained using L1-norm regularization to enhance details in partially over-and underexposed images. Extensive experiments reveal the effectives of the proposed approach as compared to other state-of-the-art algorithms. INDEX TERMS Exposure correction, low-light conditions, details enhancement, image degradation model.

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Section: B Results and Discussionmentioning

confidence: 99%

Efficient Image Enhancement Model for Correcting Uneven Illumination Images

Rahman

Aamir

et al. 2020

IEEE Access

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“…Our method was compared visually with 10 different state-of-the-art methods under real low-light images. IB (Al-Ameen, 2019 ) method based on mathematical theory method. FFM (Dai et al, 2019 ) method based on image fusion.…”

Section: Methodsmentioning

confidence: 99%

Attention-Guided Multi-Scale Feature Fusion Network for Low-Light Image Enhancement

Cui

Hua

et al. 2022

Front. Neurorobot.

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Low-light image enhancement has been an important research branch in the field of computer vision. Low-light images are characterized by poor visibility, high noise and low contrast. To improve low-light images generated in low-light environments and night conditions, we propose an Attention-Guided Multi-scale feature fusion network (MSFFNet) for low-light image enhancement for enhancing the contrast and brightness of low-light images. First, to avoid the high cost computation arising from the stacking of multiple sub-networks, our network uses a single encoder and decoder for multi-scale input and output images. Multi-scale input images can make up for the lack of pixel information and loss of feature map information caused by a single input image. The multi-scale output image can effectively monitor the error loss in the image reconstruction process. Second, the Convolutional Block Attention Module (CBAM) is introduced in the encoder part to effectively suppress the noise and color difference generated during feature extraction and further guide the network to refine the color features. Feature calibration module (FCM) is introduced in the decoder section to enhance the mapping expression between channels. Attention fusion module (AFM) is also added to capture contextual information, which is more conducive to recovering image detail information. Last, the cascade fusion module (CFM) is introduced to effectively combine the feature map information under different perceptual fields. Sufficient qualitative and quantitative experiments have been conducted on a variety of publicly available datasets, and the proposed MSFFNet outperforms other low-light enhancement methods in terms of visual effects and metric scores.

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“…wileyonlinelibrary.com/iet-ipr 535 In recent years, there has been much research on lowlight enhancement technology, there are some popular lowlight image enhancement methods, such as LIME [6], EFF [7], BIMEF [8], JED [9], MBLLEN [10], FFM [11], KinD [12], IB [13], SDD [14], Zero-DCE [15], EnGAN [16], DLN [17], MIR-Net [18]. But these studies still need to be improved.…”

Section: Introductionmentioning

confidence: 99%

“…FIGURE13 Quantitatively compare the enhancement methods of images from the LIME dataset, ExDark dataset, NPE dataset and VV dataset using BRISQUE and NIQE quality indices. Our method has a low evaluation value for multiple images, and the enhancement effect is better…”

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confidence: 99%

LBP‐based progressive feature aggregation network for low‐light image enhancement

Hua

2021

IET Image Processing

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At night or in other low-illumination environments, optical imaging devices cannot capture details and color information in images accurately because of the reduced number of photons captured and the low signal-to-noise ratio. Consequently, the image is very noisy with low contrast and inaccurate color information, which affects human visual perception and creates significant challenges in computer vision tasks. Low-light image enhancement has great research value because it aims to reduce image noise and improve image quality. In this study, we propose an LBP-based progressive feature aggregation network (P-FANet) for low-light image enhancement. The LBP feature has insensitivity to illumination, and it contains rich texture information. In the network, we input the LBP feature into each iteration of the network in an accompanying manner, which helps to restore some detailed information of the low-light image. First, we input the low-light image into the dual attention mechanism model to extract global features. Second, the extracted different features enter the feature aggregation module (FAM) for feature fusion. Third, we use the recurrent layer to share the features extracted at different stages, and use the residual layer to further extract deeper features. Finally, the enhanced image is output. The rationality of the method in this study has been verified through ablation experiments. Many experimental results show that the method in this study has greater advantages in subjective and objective evaluations compared with many other advanced methods.

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Nighttime image enhancement using a new illumination boost algorithm

Cited by 56 publications

References 20 publications

Efficient Image Enhancement Model for Correcting Uneven Illumination Images

Efficient Image Enhancement Model for Correcting Uneven Illumination Images

Attention-Guided Multi-Scale Feature Fusion Network for Low-Light Image Enhancement

LBP‐based progressive feature aggregation network for low‐light image enhancement

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