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Image Enhancement and Restoration Based on Deep Learning Technology

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 21435

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Prof. Dr. Hyo Jong Lee

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Guest Editor

Dept of Computer Science & Engineering, Jeonbuk National University, Jeonju 54896, Korea
Interests: image processing; pattern recognition; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yong Yang

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Guest Editor

School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China
Interests: image fusion; image super-resolution; image deraining; image enhancement

Special Issue Information

Dear Colleagues,

An important goal of digital imaging is producing scenes with good contrast, vivid color, and rich details. However, some issues, such as low intensity, defocusing, low resolution, object occlusions, and inclement weather, degrade the quality of images. A downgraded low-resolution image does not provide enough information in various computer vision applications. A nonlinear mapping from the low-resolution to super-resolution to reconstruct a clear and high resolution image is important and a fundamental task. Unfavorable weather causes significant problems to image quality as the rain or snow often occludes or blurs the scene information. Image enhancement and restoration are critical processes in various computer vision applications, such as security, surveillance imaging, medical imaging, image recognition, computational photography, and remote sensing. Due to the diversity of imaging sensors and mechanisms, multiple modality images may need to be fused together to enhance image quality. Recently, deep learning has been widely used in image enhancement and restoration and has achieved great success due to its superior ability to extract features. In addition, the deep convolutional neural network is also used for single image enhancement. In this Special Issue, we cover wide approaches for robust image enhancement and restoration based on deep learning technology.

Dr. Hyo Jong Lee
Prof. Dr. Yong Yang
Guest Editors

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Keywords

image enhancement
image deraining
image desnowing
attention
occlusion
deep learning
convolutional neural network
residual network
super-resolution

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Published Papers (9 papers)

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Research

19 pages, 8704 KiB

Open AccessArticle

CA-BSN: Mural Image Denoising Based on Cross–Attention Blind Spot Network

by Xingquan Cai, Yao Liu, Shike Liu, Haoyu Zhang and Haiyan Sun

Appl. Sci. 2024, 14(2), 741; https://doi.org/10.3390/app14020741 - 15 Jan 2024

Viewed by 1120

Abstract

Recently, Asymmetric pixel–shuffle downsampling and Blind–Spot Network (AP-BSN) has made some progress in unsupervised image denoising. However, the method tends to damage the texture and edge information of the image when using pixel-shuffle downsampling (PD) to destroy pixel-related large-scale noise. To tackle this issue, we suggest a denoising method for mural images based on Cross Attention and Blind–Spot Network (CA-BSN). First, the input image is downsampled using PD, and after passing through a masked convolution module (MCM), the features are extracted respectively; then, a cross attention network (CAN) is constructed to fuse the extracted feature; finally, a feed-forward network (FFN) is introduced to strengthen the correlation between the feature, and the denoised processed image is output. The experimental results indicate that our proposed CA-BSN algorithm achieves a PSNR growth of 0.95 dB and 0.15 dB on the SIDD and DND datasets, respectively, compared to the AP-BSN algorithm. Furthermore, our method demonstrates a SSIM growth of 0.7% and 0.2% on the SIDD and DND datasets, respectively. The experiments show that our algorithm preserves the texture and edge details of the mural images better than AP-BSN, while also ensuring the denoising effect. Full article

(This article belongs to the Special Issue Image Enhancement and Restoration Based on Deep Learning Technology)

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17 pages, 9783 KiB

Open AccessArticle

Low-Light Image Enhancement Method for Electric Power Operation Sites Considering Strong Light Suppression

by Yang Xi, Zihao Zhang and Wenjing Wang

Appl. Sci. 2023, 13(17), 9645; https://doi.org/10.3390/app13179645 - 25 Aug 2023

Cited by 1 | Viewed by 970

Abstract

Insufficient light, uneven light, backlighting, and other problems lead to poor visibility of the image of an electric power operation site. Most of the current methods directly enhance the low-light image while ignoring local strong light that may appear in the electric power operation site, resulting in overexposure and a poor enhancement effect. Aiming at the above problems, we propose a low-light image enhancement method for electric power operation sites by considering strong light suppression. Firstly, a sliding-window-based strong light judgment method was designed, which used a sliding window to segment the image, and a brightness judgment was performed based on the average value of the deviation and the average deviation of the subimages of the grayscale image from the strong light threshold. Then, a light effect decomposition method based on a layer decomposition network was used to decompose the light effect of RGB images with the presence of strong light and eliminate the light effect layer. Finally, a Zero-DCE (Zero-Reference Deep Curve Estimation) low-light enhancement network based on a kernel selection module was constructed to enhance the low-light images with reduced or no strong light interference. Comparison experiments using the electric power operation private dataset and the SICE (Single Image Contrast Enhancement) Part 2 public dataset showed that our proposed method outperformed the current state-of-the-art low-light enhancement methods in terms of both subjective visual effects and objective evaluation metrics, which effectively improves the image quality of electric power operation sites in low-light environments and provides excellent image bases for other computer vision tasks, such as the estimation of operators’ posture. Full article

(This article belongs to the Special Issue Image Enhancement and Restoration Based on Deep Learning Technology)

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17 pages, 4977 KiB

Open AccessArticle

A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal

by Jielin Jiang, Li Liu, Yan Cui and Yingnan Zhao

Appl. Sci. 2023, 13(17), 9520; https://doi.org/10.3390/app13179520 - 22 Aug 2023

Cited by 1 | Viewed by 1426

Abstract

Eliminating mixed noise from images is a challenging task because accurately describing the attenuation of noise distribution is difficult. However, most existing algorithms for mixed noise removal solely rely on the local information of the image and neglect the global information, resulting in suboptimal denoising performance when dealing with complex mixed noise. In this paper, we propose a nested UNet based on multi-scale feature extraction (MSNUNet) for mixed noise removal. In MSNUNet, we introduce a U-shaped subnetwork called MSU-Subnet for multi-scale feature extraction. These multi-scale features contain abundant local and global features, aiding the model in estimating noise more accurately and improving its robustness. Furthermore, we introduce a multi-scale feature fusion channel attention module (MSCAM) to effectively aggregate feature information from different scales while preserving intricate image texture details. Our experimental results demonstrate that MSNUNet achieves leading performance in terms of quality metrics and the visual appearance of images. Full article

(This article belongs to the Special Issue Image Enhancement and Restoration Based on Deep Learning Technology)

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19 pages, 12053 KiB

Open AccessArticle

Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary

by Yingmei Zhang and Hyo Jong Lee

Appl. Sci. 2023, 13(5), 2907; https://doi.org/10.3390/app13052907 - 24 Feb 2023

Cited by 1 | Viewed by 1805

Abstract

With the industrial demand caused by multi-sensor image fusion, infrared and visible image fusion (IVIF) technology is flourishing. In recent years, scale decomposition methods have led the trend for feature extraction. Such methods, however, have low time efficiency. To address this issue, this paper proposes a simple yet effective IVIF approach via a feature-oriented dual-module complementary. Specifically, we analyze five classical operators comprehensively and construct the spatial gradient capture module (SGCM) and infrared brightness supplement module (IBSM). In the SGCM, three kinds of feature maps are obtained, respectively, by introducing principal component analysis, saliency, and proposing contrast estimation operators considered the relative differences of contrast information covered by the input images. These maps are later reconstructed through pyramidal transformation to obtain the predicted image. The IBSM is then proposed to refine the missing infrared thermal information in the predicted image. Among them, we improve the measurement operators applied to the exposure modalities, namely, the gradient of the grayscale images (2D gradient) and well-exposedness. The former is responsible for extracting fine details, and the latter is meant for locating brightness regions. Experiments performed on public datasets demonstrate that the proposed method outperforms nine state-of-the-art methods in terms of subjective visual and objective indicators. Full article

(This article belongs to the Special Issue Image Enhancement and Restoration Based on Deep Learning Technology)

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15 pages, 4295 KiB

Open AccessArticle

Image Deblurring Based on an Improved CNN-Transformer Combination Network

by Xiaolin Chen, Yuanyuan Wan, Donghe Wang and Yuqing Wang

Appl. Sci. 2023, 13(1), 311; https://doi.org/10.3390/app13010311 - 27 Dec 2022

Cited by 6 | Viewed by 3176

Abstract

Recently, using a CNN has been a common practice to restore blurry images due to its strong ability to learn feature information from large-scale datasets. However, CNNs essentially belong to local operations and have the defect of a limited receptive field, which reduces the naturalness of deblurring results. Moreover, CNN-based deblurring methods usually adopt many downsample operations, which hinder detail recovery. Fortunately, transformers focus on modeling the global features, so they can cooperate with CNNs to enlarge the receptive field and compensate for the details lost as well. In this paper, we propose an improved CNN-transformer combination network for deblurring, which adopts a coarse-to-fine architecture as the backbone. To extract the local features and global features simultaneously, the common methods are two blocks connected in parallel or cascaded. Different from these, we design a local-global feature combination block (LGFCB) with a new connecting structure to better use the extracted features. The LGFCB comprises multi-scale residual blocks (MRB) and a transformer block. In addition, we adopt a channel attention fusion block (CAFB) in the encoder path to integrate features. To improve the ability of feature representation, in the decoder path, we introduce a supervised attention block (SAB) operated on restoration images to refine features. Numerous experiments on GoPro and RealBlur datasets indicated that our model achieves remarkable accuracy and processing speed. Full article

(This article belongs to the Special Issue Image Enhancement and Restoration Based on Deep Learning Technology)

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17 pages, 5094 KiB

Open AccessArticle

Rain Removal of Single Image Based on Directional Gradient Priors

by Shuying Huang, Yating Xu, Mingyang Ren, Yong Yang and Weiguo Wan

Appl. Sci. 2022, 12(22), 11628; https://doi.org/10.3390/app122211628 - 16 Nov 2022

Cited by 3 | Viewed by 1494

Abstract

Images taken on rainy days often lose a significant amount of detailed information owing to the coverage of rain streaks, which interfere with the recognition and detection of the intelligent vision systems. It is, therefore, extremely important to recover clean rain-free images from the rain images. In this paper, we propose a rain removal method based on directional gradient priors, which aims to retain the structural information of the original rain image to the greatest extent possible while removing the rain streaks. First, to solve the problem of residual rain streaks, on the basis of the sparse convolutional coding model, two directional gradient regularization terms are proposed to constrain the direction information of the rain stripe. Then, for the rain layer coding in the directional gradient prior terms, a multi-scale dictionary is designed for convolutional sparse coding to detect rain stripes of different widths. Finally, to obtain a more accurate solution, the alternating direction method of multipliers (ADMM) is used to update the multi-scale dictionary and coding coefficients alternately to obtain a rainless image with rich details. Finally, experiments verify that the proposed algorithm achieves good results both subjectively and objectively. Full article

(This article belongs to the Special Issue Image Enhancement and Restoration Based on Deep Learning Technology)

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21 pages, 7273 KiB

Open AccessArticle

Deep Neural Network Concept for a Blind Enhancement of Document-Images in the Presence of Multiple Distortions

by Kabeh Mohsenzadegan, Vahid Tavakkoli and Kyandoghere Kyamakya

Appl. Sci. 2022, 12(19), 9601; https://doi.org/10.3390/app12199601 - 24 Sep 2022

Cited by 2 | Viewed by 2387

Abstract

In this paper, we propose a new convolutional neural network (CNN) architecture for improving document-image quality through decreasing the impact of distortions (i.e., blur, shadows, contrast issues, and noise) contained therein. Indeed, for many document-image processing systems such as OCR (optical character recognition) and document-image classification, the real-world image distortions can significantly degrade the performance of such systems in a way such that they become merely unusable. Therefore, a robust document-image enhancement model is required to preprocess the involved document images. The preprocessor system developed in this paper places “deblurring” and “noise removal and contrast enhancement” in two separate and sequential submodules. In the architecture of those two submodules, three new parts are introduced: (a) the patch-based approach, (b) preprocessing layer involving Gabor and Blur filters, and (c) the approach using residual blocks. Using these last-listed innovations results in a very promising performance when compared to the related works. Indeed, it is demonstrated that even extremely strongly degraded document images that were not previously recognizable by an OCR system can now become well-recognized with a 91.51% character recognition accuracy after the image enhancement preprocessing through our new CNN model. Full article

(This article belongs to the Special Issue Image Enhancement and Restoration Based on Deep Learning Technology)

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13 pages, 1719 KiB

Open AccessArticle

Lung’s Segmentation Using Context-Aware Regressive Conditional GAN

by Zakir Khan, Arif Iqbal Umar, Syed Hamad Shirazi, Assad Rasheed, Waqas Yousaf, Muhammad Assam, Izaz Hassan and Abdullah Mohamed

Appl. Sci. 2022, 12(12), 5768; https://doi.org/10.3390/app12125768 - 7 Jun 2022

Cited by 2 | Viewed by 1696

Abstract

After declaring COVID-19 pneumonia as a pandemic, researchers promptly advanced to seek solutions for patients fighting this fatal disease. Computed tomography (CT) scans offer valuable insight into how COVID-19 infection affects the lungs. Analysis of CT scans is very significant, especially when physicians are striving for quick solutions. This study successfully segmented lung infection due to COVID-19 and provided a physician with a quantitative analysis of the condition. COVID-19 lesions often occur near and over parenchyma walls, which are denser and exhibit lower contrast than the tissues outside the parenchyma. We applied Adoptive Wallis and Gaussian filter alternatively to regulate the outlining of the lungs and lesions near the parenchyma. We proposed a context-aware conditional generative adversarial network (CGAN) with gradient penalty and spectral normalization for automatic segmentation of lungs and lesion segmentation. The proposed CGAN implements higher-order statistics when compared to traditional deep-learning models. The proposed CGAN produced promising results for lung segmentation. Similarly, CGAN has shown outstanding results for COVID-19 lesions segmentation with an accuracy of 99.91%, DSC of 92.91%, and AJC of 92.91%. Moreover, we achieved an accuracy of 99.87%, DSC of 96.77%, and AJC of 95.59% for lung segmentation. Additionally, the suggested network attained a sensitivity of 100%, 81.02%, 76.45%, and 99.01%, respectively, for critical, severe, moderate, and mild infection severity levels. The proposed model outperformed state-of-the-art techniques for the COVID-19 segmentation and detection cases. Full article

(This article belongs to the Special Issue Image Enhancement and Restoration Based on Deep Learning Technology)

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16 pages, 1604 KiB

Open AccessArticle

A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning

by Muhammad Adnan, Muhammad Sardaraz, Muhammad Tahir, Muhammad Najam Dar, Mona Alduailij and Mai Alduailij

Appl. Sci. 2022, 12(11), 5700; https://doi.org/10.3390/app12115700 - 3 Jun 2022

Cited by 6 | Viewed by 4567

Abstract

Iris detection and tracking plays a vital role in human–computer interaction and has become an emerging field for researchers in the last two decades. Typical applications such as virtual reality, augmented reality, gaze detection for customer behavior, controlling computers, and handheld embedded devices need accurate and precise detection of iris landmarks. A significant improvement has been made so far in iris detection and tracking. However, iris landmarks detection in real-time with high accuracy is still a challenge and a computationally expensive task. This is also accompanied with the lack of a publicly available dataset of annotated iris landmarks. This article presents a benchmark dataset and a robust framework for the localization of key landmark points to extract the iris with better accuracy. A number of training sessions have been conducted for MobileNetV2, ResNet50, VGG16, and VGG19 over an iris landmarks dataset, and ImageNet weights are used for model initialization. The Mean Absolute Error (MAE), model loss, and model size are measured to evaluate and validate the proposed model. Results analyses show that the proposed model outperforms other methods on selected parameters. The MAEs of MobileNetV2, ResNet50, VGG16, and VGG19 are 0.60, 0.33, 0.35, and 0.34; the average decrease in size is 60%, and the average reduction in response time is 75% compared to the other models. We collected the images of eyes and annotated them with the help of the proposed algorithm. The generated dataset has been made publicly available for research purposes. The contribution of this research is a model with a more diminutive size and the real-time and accurate prediction of iris landmarks, along with the provided dataset of iris landmark annotations. Full article

(This article belongs to the Special Issue Image Enhancement and Restoration Based on Deep Learning Technology)

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