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Advances in Image Enhancement and Restoration 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 (31 December 2024) | Viewed by 12137

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Special Issue Editors

Prof. Dr. Xiaofang Hu

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Guest Editor

College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: image processing; memristive neural network; deep learning

Dr. Yun Liu

E-Mail Website
Guest Editor

College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: image enhancement; image restoration; deep learning; artificial intelligence
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Dear Colleagues,

Computer vision has extensive applications in various fields, including areas such as outdoor navigation, security surveillance, object detection, underwater exploration, and target recognition. Possessing a high-quality clear image is crucial if computer vision systems are to obtain accurate visual information. However, under various complex imaging conditions, such as fog, haze, rain, snow, low-light and underwater environments, the acquired images suffer from severe color distortion, scene blurring, and poor clarity, significantly impacting their applications and restricting related research in these fields.

The goal of this Special Issue is to explore the recent advances in the field of image enhancement and restoration. Enhancing and restoring low-quality images affected by adverse weather or other conditions is crucial for achieving accurate and reliable visual information extraction. This Special Issue aims to bring together researchers and experts to share their innovative approaches, methodologies, and findings in addressing the challenges and advancing the state of the art in this field.

Prof. Dr. Xiaofang Hu
Dr. Yun Liu
Guest Editors

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Keywords

image enhancement
restoration techniques
adverse weather
image quality assessment

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Published Papers (9 papers)

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Research

17 pages, 2335 KiB

Open AccessArticle

Attention-Based Color Difference Perception for Photographic Images

by Hua Qiang, Xuande Zhang and Jinliang Hou

Appl. Sci. 2025, 15(5), 2704; https://doi.org/10.3390/app15052704 - 3 Mar 2025

Viewed by 174

Abstract

Traditional color difference (CD) measurement methods cannot adapt to large sizes and complex content of photographic images. Existing deep learning-based CD measurement algorithms only focus on local features and cannot accurately simulate the human perception of CD. The objective of this paper is to propose a high-precision image CD measurement model that simulates the perceptual process of the human visual system and apply it to the CD perception of smartphone photography images. Based on this, a CD measurement network called CD-Attention is proposed, which integrates CNN and Vision Transformer features. First, a CNN and the ViT are used separately to extract local features and global semantic features from the reference image and the distorted image. Secondly, deformable convolution is used for attention guidance, utilizing the global semantic features of the ViT to direct CNN to focus on salient regions of the image, enhancing the transformation modeling capability of CNN features. Thirdly, through the feature fusion module, the CNN features that have been guided by attention are fused with the global semantic features of the ViT. Finally, a dual-branch network for high-frequency and low-frequency predictions is used for score estimation, and the final score is obtained through a weighted sum. Validated on the large-scale SPCD dataset, the CD-Attention model has achieved state-of-the-art performance, outperforming 30 existing CD measurement methods and demonstrating useful generalization ability. It has been demonstrated that CD-Attention can achieve CD measurement for large-sized and content-complex smartphone photography images. At the same time, the effectiveness of CD-Attention’s feature extraction and attention guidance are verified by ablation experiments. Full article

(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)

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21 pages, 9210 KiB

Open AccessArticle

sRrsR-Net: A New Low-Light Image Enhancement Network via Raw Image Reconstruction

by Zhiyong Hong, Dexin Zhen, Liping Xiong, Xuechen Li and Yuhan Lin

Appl. Sci. 2025, 15(1), 361; https://doi.org/10.3390/app15010361 - 2 Jan 2025

Viewed by 662

Abstract

Most existing low-light image enhancement (LIE) methods are primarily designed for human-vision-friendly image formats, such as sRGB, due to their convenient storage and smaller file sizes. In addition, raw images provide greater detail and a wider dynamic range, which makes them more suitable for LIE tasks. Despite these advantages, raw images, the original format captured by cameras, are larger and less accessible and are hard to use in methods of LIE with mobile devices. In order to leverage both the advantages of sRGB and raw domains while avoiding the direct use of raw images as training data, this paper introduces sRrsR-Net, a novel framework with the image translation process of sRGB–raw–sRGB for LIE task. In our approach, firstly, the RGB-to-iRGB module is designed to convert sRGB images into intermediate RGB feature maps. Then, with these intermediate feature maps, to bridge the domain gap between sRGB and raw pixels, the RAWFormer module is proposed to employ global attention to effectively align features between the two domains to generate reconstructed raw images. For enhancing the raw images and restoring them back to normal-light sRGB, unlike traditional Image Signal Processing (ISP) pipelines, which are often bulky and integrate numerous processing steps, we propose the RRAW-to-sRGB module. This module simplifies the process by focusing only on color correction and white balance, while still delivering competitive results. Extensive experiments on four benchmark datasets referring to both domains demonstrate the effectiveness of our approach. Full article

(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)

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25 pages, 6883 KiB

Open AccessArticle

Hybrid Frequency–Spatial Domain Learning for Image Restoration in Under-Display Camera Systems Using Augmented Virtual Big Data Generated by the Angular Spectrum Method

by Kibaek Kim, Yoon Kim and Young-Joo Kim

Appl. Sci. 2025, 15(1), 30; https://doi.org/10.3390/app15010030 - 24 Dec 2024

Viewed by 742

Abstract

In the rapidly advancing realm of mobile technology, under-display camera (UDC) systems have emerged as a promising solution for achieving seamless full-screen displays. Despite their innovative potential, UDC systems face significant challenges, including low light transmittance and pronounced diffraction effects that degrade image quality. This study aims to address these issues by examining degradation phenomena through optical simulation and employing a deep neural network model incorporating hybrid frequency–spatial domain learning. To effectively train the model, we generated a substantial synthetic dataset that virtually simulates the unique image degradation characteristics of UDC systems, utilizing the angular spectrum method for optical simulation. This approach enabled the creation of a diverse and comprehensive dataset of virtual degraded images by accurately replicating the degradation process from pristine images. The augmented virtual data were combined with actual degraded images as training data, compensating for the limitations of real data availability. Through our proposed methods, we achieved a marked improvement in image quality, with the average structural similarity index measure (SSIM) value increasing from 0.8047 to 0.9608 and the peak signal-to-noise ratio (PSNR) improving from 26.383 dB to 36.046 dB on an experimentally degraded image dataset. These results highlight the potential of our integrated optics and AI-based methodology in addressing image restoration challenges within UDC systems and advancing the quality of display technology in smartphones. Full article

(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)

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15 pages, 7532 KiB

Open AccessArticle

Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition

by Libang Chen, Jinyan Lin, Qihang Bian, Yikun Liu and Jianying Zhou

Appl. Sci. 2024, 14(22), 10111; https://doi.org/10.3390/app142210111 - 5 Nov 2024

Viewed by 1054

Abstract

Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition, thereby hindering conventional image processing methods. Despite improvements through neural network-based approaches, these techniques falter under extremely low visibility conditions exacerbated by inhomogeneous illumination, which degrades deep learning performance due to inconsistent signal intensities. We introduce in this paper a novel method that adaptively filters background illumination based on Structural Differential and Integral Filtering (SDIF) to enhance only the vital signal information. The grayscale banding is eliminated by incorporating a visual optimization strategy based on image gradients. Maximum Histogram Equalization (MHE) is used to achieve high contrast while maintaining fidelity to the original content. We evaluated our algorithm using data collected from both a fog chamber and outdoor environments and performed comparative analyses with existing methods. Our findings demonstrate that our proposed method significantly enhances signal clarity under extremely low visibility conditions and out-performs existing techniques, offering substantial improvements for deep fog imaging applications. Full article

(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)

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20 pages, 5032 KiB

Open AccessArticle

Enhanced Learning Enriched Features Mechanism Using Deep Convolutional Neural Network for Image Denoising and Super-Resolution

by Iqra Waseem, Muhammad Habib, Eid Rehman, Ruqia Bibi, Rehan Mehmood Yousaf, Muhammad Aslam, Syeda Fizzah Jilani and Muhammad Waqar Younis

Appl. Sci. 2024, 14(14), 6281; https://doi.org/10.3390/app14146281 - 18 Jul 2024

Viewed by 1678

Abstract

Image denoising and super-resolution play vital roles in imaging systems, greatly reducing the preprocessing cost of many AI techniques for object detection, segmentation, and tracking. Various advancements have been accomplished in this field, but progress is still needed. In this paper, we have proposed a novel technique named the Enhanced Learning Enriched Features (ELEF) mechanism using a deep convolutional neural network, which makes significant improvements to existing techniques. ELEF consists of two major processes: (1) Denoising, which removes the noise from images; and (2) Super-resolution, which improves the clarity and details of images. Features are learned through deep CNN and not through traditional algorithms so that we can better refine and enhance images. To effectively capture features, the network architecture adopted Dual Attention Units (DUs), which align with the Multi-Scale Residual Block (MSRB) for robust feature extraction, working sidewise with the feature-matching Selective Kernel Extraction (SKF). In addition, resolution mismatching cases are processed in detail to produce high-quality images. The effectiveness of the ELEF model is highlighted by the performance metrics, achieving a Peak Signal-to-Noise Ratio (PSNR) of 42.99 and a Structural Similarity Index (SSIM) of 0.9889, which indicates the ability to carry out the desired high-quality image restoration and enhancement. Full article

(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)

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13 pages, 4733 KiB

Open AccessArticle

Haze-Aware Attention Network for Single-Image Dehazing

by Lihan Tong, Yun Liu, Weijia Li, Liyuan Chen and Erkang Chen

Appl. Sci. 2024, 14(13), 5391; https://doi.org/10.3390/app14135391 - 21 Jun 2024

Cited by 4 | Viewed by 1649

Abstract

Single-image dehazing is a pivotal challenge in computer vision that seeks to remove haze from images and restore clean background details. Recognizing the limitations of traditional physical model-based methods and the inefficiencies of current attention-based solutions, we propose a new dehazing network combining an innovative Haze-Aware Attention Module (HAAM) with a Multiscale Frequency Enhancement Module (MFEM). The HAAM is inspired by the atmospheric scattering model, thus skillfully integrating physical principles into high-dimensional features for targeted dehazing. It picks up on latent features during the image restoration process, which gives a significant boost to the metrics, while the MFEM efficiently enhances high-frequency details, thus sidestepping wavelet or Fourier transform complexities. It employs multiscale fields to extract and emphasize key frequency components with minimal parameter overhead. Integrated into a simple U-Net framework, our Haze-Aware Attention Network (HAA-Net) for single-image dehazing significantly outperforms existing attention-based and transformer models in efficiency and effectiveness. Tested across various public datasets, the HAA-Net sets new performance benchmarks. Our work not only advances the field of image dehazing but also offers insights into the design of attention mechanisms for broader applications in computer vision. Full article

(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)

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18 pages, 15807 KiB

Open AccessArticle

Enhanced Solar Coronal Imaging: A GAN Approach with Fused Attention and Perceptual Quality Enhancement

by Zhenhong Shang and Ruiyao Li

Appl. Sci. 2024, 14(10), 4054; https://doi.org/10.3390/app14104054 - 10 May 2024

Cited by 1 | Viewed by 1264

Abstract

The activity of the solar corona has a significant impact on all aspects of human life. People typically use images obtained from astronomical telescopes to observe coronal activities, among which the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) is particularly widely used. However, due to resolution limitations, we have begun to study the application of generative adversarial network super-resolution techniques to enhance the image data quality for a clearer observation of the fine structures and dynamic processes in the solar atmosphere, which improves the prediction accuracy of solar activities. We aligned SDO/AIA images with images from the High-Resolution Coronal Imager (Hi-C) to create a dataset. This research proposes a new super-resolution method named SAFCSRGAN, which includes a spatial attention module that incorporates channel information, allowing the network model to better capture the corona’s features. A Charbonnier loss function was introduced to enhance the perceptual quality of the super-resolution images. Compared to the original method using ESRGAN, our method achieved an 11.9% increase in Peak Signal-to-Noise Ratio (PSNR) and a 4.8% increase in Structural Similarity (SSIM). Additionally, we introduced two perceptual image quality assessment metrics, the Natural Image Quality Evaluator (NIQE) and Learned Perceptual Image Patch Similarity (LPIPS), which improved perceptual quality by 10.8% and 1.3%, respectively. Finally, our experiments demonstrated that our improved model surpasses other models in restoring the details of coronal images. Full article

(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)

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15 pages, 1091 KiB

Open AccessArticle

Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation

by Yunzhuo Liu, Chunjiang Wu, Yuting Zeng, Keyu Chen and Shijie Zhou

Appl. Sci. 2023, 13(24), 13226; https://doi.org/10.3390/app132413226 - 13 Dec 2023

Cited by 3 | Viewed by 2256

Abstract

Artificial Intelligence has been widely applied in intelligent transportation systems. In this work, Swin-APT, a deep learning-based approach for semantic segmentation and object detection in intelligent transportation systems is presented. Swin-APT includes a lightweight network and a multiscale adapter network designed for image semantic segmentation and object detection tasks. An inter-frame consistency module is proposed to extract more accurate road information from images. Experimental results on four datasets: BDD100K, CamVid, SYNTHIA, and CeyMo, demonstrate that Swin-APT outperforms the baseline by 13.1%. Furthermore, experiments on the road marking detection benchmark show an improvement of 1.85% of mAcc. Full article

(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)

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22 pages, 32278 KiB

Open AccessArticle

LPS-Net: Lightweight Parallel Strategy Network for Underwater Image Enhancement

by Jingxia Jiang, Peiyun Huang, Lihan Tong, Junjie Yin and Erkang Chen

Appl. Sci. 2023, 13(16), 9419; https://doi.org/10.3390/app13169419 - 19 Aug 2023

Viewed by 1199

Abstract

Underwater images are frequently subject to color distortion and loss of details. However, previous enhancement methods did not tackle these mixed degradations by dividing them into sub-problems that could be effectively addressed. Moreover, the parameters and computations required for these methods are usually costly for underwater equipment, which has limited power supply, processing capabilities, and memory capacity. To address these challenges, this work proposes a Lightweight Parallel Strategy Network (LPS-Net). Firstly, a Dual-Attention Enhancement Block and a Mirror Large Receptiveness Block are introduced to, respectively, enhance the color and restore details in degraded images. Secondly, we employed these blocks on parallel branches at each stage of LPS-Net, with the goal of achieving effective image color and detail rendering simultaneously. Thirdly, a Gated Fusion Unit is proposed to merge features from different branches at each stage. Finally, the network utilizes four stages of parallel enhancement, achieving a balanced trade-off between performance and parameters. Extensive experiments demonstrated that LPS-Net achieves optimal color enhancement and superior detail restoration in terms of visual quality. Furthermore, it attains state-of-the-art underwater image enhancement performance on the evaluation metrics, while using only 80.12 k parameters. Full article

(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)

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