Image Enhancement Approach Research Articles

Deep autoencoder based image enhancement approach with hybrid feature extraction for plant disease detection using supervised classification

Plant leaf diseases pose significant threats to global agriculture, leading to reduced crop yields and economic losses. Rapid and accurate disease detection is essential for timely interventions and sustainable farming practices. This study presents an innovative approach for plant leaf disease detection by integrating wavelet analysis, color, and texture features, coupled with autoencoder denoising and support vector machine (SVM) classification. Wavelet analysis is employed to extract multi-resolution features, capturing intricate details at different scales. Furthermore, color and texture characteristics are extracted to encompass a broad spectrum of visual information crucial for distinguishing diseases. The Autoencoder model helps to enhance the feature representation that mitigates the impact of noise and irrelevant data. The SVM classifier is utilized to learn complex patterns and accurately classify different disease classes. The combined model of wavelet, color, and texture attributes, in combination with autoencoder denoising and SVM classification, markedly enhances the precision and efficiency of disease detection in contrast to conventional methods. The system's performance is evaluated using a PlantVillage dataset, showcasing its adaptability to different plant species and disease types. The overall performance is obtained as 98.60%, 97.25%, 96.89%, and 97.20% in terms of accuracy, precision, recall, and F-Score, respectively.

Wavelet-based Filter Bank Approach for Image Compression and Enhancement in Alzheimer\'s Disease Diagnosis Using Medical Imaging

Abstract: This research presents a wavelet-based filter bank approach for image compression and enhancement in the context of Alzheimer's disease diagnosis using medical imaging. The proposed system leverages the PyWavelets and OpenCV libraries in Python to implement Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) algorithms. The primary objective is to develop an efficient image compression technique while preserving visual quality and enhancing relevant features for improved diagnostic accuracy. The system utilizes filter banks to process wavelet coefficients, enabling selective enhancement of patterns or biomarkers associated with Alzheimer's disease. Results show that the decompressed images closely resemble the originals, with low Mean Squared Error (MSE) and high Peak Signal-to-Noise Ratio (PSNR) values. Visual inspection and quantitative metrics confirm the successful preservation of image quality and structural details. The wavelet coefficients visualization highlights the captured significant features crucial for reconstruction

Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks

Landslides are a kind of geological hazard with great destructive potential. When a landslide event occurs, a reliable landslide segmentation method is important for assessing the extent of the disaster and preventing secondary disasters. Although deep learning methods have been applied to improve the efficiency of landslide segmentation, there are still some problems that need to be solved, such as the poor segmentation due to the similarity between old landslide areas and the background features and missed detections of small-scale landslides. To tackle these challenges, a proposed high-resolution semantic segmentation algorithm for landslide scenes enhances the accuracy of landslide segmentation and addresses the challenge of missed detections in small-scale landslides. The network is based on the high-resolution network (HR-Net), which effectively integrates the efficient channel attention mechanism (efficient channel attention, ECA) into the network to enhance the representation quality of the feature maps. Moreover, the primary backbone of the high-resolution network is further enhanced to extract more profound semantic information. To improve the network’s ability to perceive small-scale landslides, atrous spatial pyramid pooling (ASPP) with ECA modules is introduced. Furthermore, to address the issues arising from inadequate training and reduced accuracy due to the unequal distribution of positive and negative samples, the network employs a combined loss function. This combined loss function effectively supervises the training of the network. Finally, the paper enhances the Loess Plateau landslide dataset using a fractional-order-based image enhancement approach and conducts experimental comparisons on this enriched dataset to evaluate the enhanced network’s performance. The experimental findings show that the proposed methodology achieves higher accuracy in segmentation performance compared to other networks.

An effective underground image enhancement approach based on improved KinD network

An effective underground image enhancement approach based on improved KinD network

LVIF-Net: Learning synchronous visible and infrared image fusion and enhancement under low-light conditions

In recent years, the fusion of infrared and visible images has gained significant prominence in the effort to seamlessly integrate salient objects and intricate textures within the fused imagery. While numerous fusion techniques have demonstrated commendable performance, the image quality is notably compromised in low-light environments. This predicament can be attributed to insufficient feature extraction, the inherent low-illumination characteristics of the source images, and the suppression of dark backgrounds. In this study, we introduce a novel approach for simultaneous image fusion and enhancement, aiming to achieve robust fusion performance even in low-light conditions. Our proposed method involves the development of an image fusion network, known as LVIF-Net, which is designed to facilitate multi-layer feature interactions for the enhancement of complementary low-light visible and infrared feature extraction. To solve the problem of low illumination in fused images, we further introduce an Image Decomposition Network (IDN) to guide the LVIF-Net to correct the illumination component of the fused image to obtain enhanced fused images. In addition, for coping with influence of dark background, we design a local region content loss to enhance infrared information in different intensity regions between source images. Extensive experiments on the low-light images on LLVIP and MFNet datasets have shown that the proposed method achieves significant advantages under low-light conditions.

Nakshatra-Drishti: A Supervised Learning Approach for Low Light Image Enhancement Using Convolutional Neural Networks

Nakshatra-Drishti: A Supervised Learning Approach for Low Light Image Enhancement Using Convolutional Neural Networks

Image enhancement with intensity transformation on embedding space

AbstractIn recent times, an image enhancement approach, which learns the global transformation function using deep neural networks, has gained attention. However, many existing methods based on this approach have a limitation: their transformation functions are too simple to imitate complex colour transformations between low‐quality images and manually retouched high‐quality images. In order to address this limitation, a simple yet effective approach for image enhancement is proposed. The proposed algorithm based on the channel‐wise intensity transformation is designed. However, this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours. To this end, the authors define the continuous intensity transformation (CIT) to describe the mapping between input and output intensities on the embedding space. Then, the enhancement network is developed, which produces multi‐scale feature maps from input images, derives the set of transformation functions, and performs the CIT to obtain enhanced images. Extensive experiments on the MIT‐Adobe 5K dataset demonstrate that the authors’ approach improves the performance of conventional intensity transforms on colour space metrics. Specifically, the authors achieved a 3.8% improvement in peak signal‐to‐noise ratio, a 1.8% improvement in structual similarity index measure, and a 27.5% improvement in learned perceptual image patch similarity. Also, the authors’ algorithm outperforms state‐of‐the‐art alternatives on three image enhancement datasets: MIT‐Adobe 5K, Low‐Light, and Google HDR+.

Hybrid Simulated Annealing‐Evaporation Rate‐Based Water Cycle Algorithm Application for Medical Image Enhancement

Visualizing medical images is difficult due to artifacts, poor local contrast, low soft tissue contrast, excessive noise levels, and a wide dynamic range. This has created a serious problem for physicians, resulting in unapproachable and inaccurate disease diagnoses. To circumvent this problem, this research proposes a medical image enhancement (MIE) approach based on the hybrid simulated annealing‐evaporation rate‐based water cycle algorithm (SA‐ERWCA). The ERWCA enhances distorted medical images by finding the best optimal solution for the transformation parameters according to the fitness function. The fitness function consists of three objective measurements, namely, entropy, number of edges, and sum of edge intensities. Due to its high potential for finding a globally optimal solution, SA is applied solely to determine the optimized initial population of the ERWCA, thereby enhancing its convergence characteristics. To simply put, the main objective of SA is to avoid premature convergence of the ERWCA. Thus, blending SA and ERWCA produces better‐quality medical images. The performance of the proposed algorithm was compared to histogram equalization (HE), low contrast stretching (LCS), contrast‐limited adaptive histogram equalization (CLAHE), particle swarm optimization (PSO), accelerated PSO (APSO), and the water cycle algorithm (WCA). Along with the objective function fitness, seven full reference (FR) image quality assessment (IQA) metrics were implemented to evaluate image quality and compare performance. The findings of the present study showed that the proposed strategy outperforms all of the compared methods in terms of objective function fitness and perceptual visual IQA metrics such as the Harr wavelet‐based perceptual similarity index (HaarPSI), visual signal‐to‐noise ratio (VSNR), and information content weighted structural similarity index measure (IW‐SSIM). The suggested technique also exhibited better stability and faster convergence time to the optimum solution. Furthermore, the proposed approach outperformed others by avoiding premature convergence to the best solution and providing excellent optimization results with acceptable computational efficiency.

An Improved Underwater Image Enhancement Approach for Border Security

An Improved Underwater Image Enhancement Approach for Border Security

Underwater Image Enhancement Based on Color Feature Fusion

The ever-changing underwater environment, coupled with the complex degradation modes of underwater images, poses numerous challenges to underwater image enhancement efforts. Addressing the issues of low contrast and significant color deviations in underwater images, this paper presents an underwater image enhancement approach based on color feature fusion. By leveraging the properties of light propagation underwater, the proposed model employs a multi-channel feature extraction strategy, using convolution blocks of varying sizes to extract features from the red, green, and blue channels, thus effectively learning both global and local information of underwater images. Moreover, an attention mechanism is incorporated to design a residual enhancement module, augmenting the capability of feature representation. Lastly, a dynamic feature enhancement module is designed using deformable convolutions, enabling the network to capture underwater scene information with higher precision. Experimental results on public datasets demonstrate the outstanding performance of our proposed method in underwater image enhancement. Further, object detection experiments conducted on pre- and post-enhanced images underscore the value of our method for downstream tasks.

Low-Light Image Enhancement with Wavelet-Based Diffusion Models

Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. To address these issues, we propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL. Specifically, we present a wavelet-based conditional diffusion model (WCDM) that leverages the generative power of diffusion models to produce results with satisfactory perceptual fidelity. Additionally, it also takes advantage of the strengths of wavelet transformation to greatly accelerate inference and reduce computational resource usage without sacrificing information. To avoid chaotic content and diversity, we perform both forward diffusion and denoising in the training phase of WCDM, enabling the model to achieve stable denoising and reduce randomness during inference. Moreover, we further design a high-frequency restoration module (HFRM) that utilizes the vertical and horizontal details of the image to complement the diagonal information for better fine-grained restoration. Extensive experiments on publicly available real-world benchmarks demonstrate that our method outperforms the existing state-of-the-art methods both quantitatively and visually, and it achieves remarkable improvements in efficiency compared to previous diffusion-based methods. In addition, we empirically show that the application for low-light face detection also reveals the latent practical values of our method. Code is available at https://github.com/JianghaiSCU/Diffusion-Low-Light.

Image Enhancement Approach for the Underwater Images Using the Optimized Color Balancing Model

Marine resources are one of the valuable resources that need to be explored and protected, but are affected due to environmental factors. The underwater images are distorted due to the light scattering and absorption phenomenon, which draws the attention of the researcher to restoring the underwater images which is a challenging task. High-quality underwater images help us to learn and study deep-sea environments, and also help in gaining deep knowledge about the ocean resources. In this research, deep learning methods are used for obtaining high-quality underwater images. Accordingly, a channel-based white balancing and social situation-aware guard optimization (WBSSGO) dependent multi-variant balancing color correction model is proposed by integrating the grouping behavior and adaptability nature of the guards and flocks, which acts as the search agents in the proposed optimization. A multi-variant-based white balancing model is employed for balancing the channels and correcting the distorted underwater images. The performance of the developed model is validated using PIQE, BRISQUE, and UCIQE, which acquired minimal values of 27.202, 24.692, and 29.725, respectively with comparative methods, while the higher value of SDME is 64.049 dB for the proposed method, which is reported to acquire the higher visual quality of underwater images.

Fusion based Image Enhancement Approach for Brain Tumor Detection

Magnetic Resonance Imaging (MRI), is a crucial technology used in the processing of medical images that provides insights into the anatomy of soft organs in the human body and helps in detecting brain tumors and spinal tumors. Despite advances in technology, most images have intrinsic drawbacks such as reduced contrast and brightness, and noise. Several contrast enhancement techniques are used such as, HE, BBHE, DSIHE, CLAHE, RMSHE, and their fusion, have been deployed on different MRI images to handle these problems. Metrics such as, entropy, PIQE and BRISQUE are used in the assessment of the results. Through the different fusion combinations, most prominent results are obtained from CLAHE-RMSHE fusion with an entropy value of 6.2516 and BRISQUE value of 40.14.

Investigation of Image Enhancement Techniques for Advancing Colon Cancer Diagnosis

Colorectal cancer continues to pose a substantial worldwide health challenge, necessitating the development of advanced imaging techniques for early and accurate diagnosis. In this study, we propose a novel hybrid image enhancement approach that combines Total Variation (TV) regularization and shift-invariant filtering to improve the visibility and diagnostic quality of colon cancer images.
 The Total Variation regularization technique is employed to effectively reduce noise and enhance the edges in the input images, thereby preserving important structural details. Simultaneously, shift-invariant filtering is utilized to address spatial variations and artifacts that often arise in medical images, ensuring consistent and reliable enhancements across the entire image. Our hybrid approach synergistically integrates the strengths of both TV regularization and shift-invariant filtering, resulting in enhanced colon cancer images that offer improved contrast, reduced noise, and enhanced fine structures. This improved image quality aids medical professionals in better identifying and characterizing cancerous lesions, ultimately leading to more accurate and timely diagnoses.
 To evaluate the effectiveness of the proposed approach, we conducted extensive experimentations on a diverse dataset of colon cancer images. Quantitative and qualitative assessments demonstrate that our hybrid approach outperforms existing enhancement methods, leading to superior image quality and diagnostic accuracy. In conclusion, the hybrid image enhancement approach presented in this study offers a promising solution for enhancing colon cancer images, contributing to the early detection and effective management of this life-threatening disease. These advancements hold significant potential for improving patient outcomes and reducing the burden of colon cancer on healthcare systems worldwide.

Low-light Image Enhancement Method Based on Illumination Curve Adjustment

This paper presents an advanced low-light image enhancement approach based on Zero-Reference Deep Curve Estimation. The technique enhances images through: (1) the integration of the Dynamic Stochastic Resonance equation for enhanced contrast and brightness, leveraging adjustable parameters for optimal image quality, and (2) application of an Anisotropic Diffusion function for image denoising and smoothing, preserving edges and texture details while effectively removing noise. This adaptive algorithm tailors diffusion intensity to local image features, further refining the resulting image quality. Experimental results confirm the efficacy of our approach in improving visual quality and noise removal in low-light images.

A novel deep learning based underwater image de-noising and detecting suspicious object

Exploration of underwater resource play a vital role for nation development. Underwater surveillance systems play a crucial role in security applications, requiring accurate detection of suspicious objects in underwater images. However, the presence of noise, poor visibility, and uneven lighting conditions in underwater environments pose significant challenges for reliable object detection. This work proposes an integrated approach for underwater image de-noising, pre-processing, enhancement, and subsequent suspicious object detection by combining the DnCNN (Deep Convolutional Neural Network), CLAHE (Contrast Limited Adaptive Histogram Equalization), and additional image enhancement techniques. In addition to de-noising and pre-processing, it incorporate various image enhancement techniques to further improve object detection performance. These techniques include color correction, contrast adjustment, and edge enhancement, aiming to enhance the visual characteristics and saliency of suspicious objects in underwater images. To evaluate the effectiveness of proposed approach, this work conducted extensive experiments on an underwater image dataset containing diverse scenes and suspicious objects. The work compares proposed method with existing de-noising, preprocessing, and object detection techniques, analyzing the results using quantitative performance metrics, including precision, recall, and F1 score. The experimental results demonstrate that proposed integrated approach outperforms individual methods and achieves superior detection performance by enhancing the quality of underwater images and improving the visibility of suspicious objects.

Infrared image enhancement based on adaptive non-local filter and local contrast

High-quality infrared images urgently needed in military and civilian fields are always associated with appropriate contrast. However, the main challenge to obtain high-quality infrared images lies in enhancement of the contrast effectively without over-enhancement of the background and noise. Hereby, an effective infrared image enhancement approach is proposed and based on an adaptive non-local filter and local contrast. An input infrared image is detected by noisy adaptive detection to separate noisy pixels, which are filtered by non-local means filter to acquire denoised image. The grayscale histogram of the denoised image is divided into foreground and background based on the local minima value. The foreground part is enhanced by local contrast weighted distribution and max entropy gamma correction. The background part is processed by linear mapping to project the grayscale to an appropriate region. Finally, the enhanced infrared image is obtained by remapping the processed foreground and background histogram. Extensive experiments on public and homemade datasets demonstrate that our method achieves 46.8183 on image clarity, which expresses its superiority for infrared image enhancement.

Underwater image enhancement via red channel maximum attenuation prior and multi-scale detail fusion.

The underwater environment poses great challenges, which have a negative impact on the capture and processing of underwater images. However, currently underwater imaging systems cannot adapt to various underwater environments to guarantee image quality. To address this problem, this paper designs an efficient underwater image enhancement approach that gradually adjusts colors, increases contrast, and enhances details. Based on the red channel maximum attenuation prior, we initially adjust the blue and green channels and correct the red channel from the blue and green channels. Subsequently, the maximum and minimum brightness blocks are estimated in multiple channels to globally stretch the image, which also includes our improved guided noise reduction filtering. Finally, in order to amplify local details without affecting the naturalness of the results, we use a pyramid fusion model to fuse local details extracted from two methods, taking into account the detail restoration effect of the optical model. The enhanced underwater image through our method has rich colors without distortion, effectively improved contrast and details. The objective and subjective evaluations indicate that our approach surpasses the state-of-the-art methods currently. Furthermore, our approach is versatile and can be applied to diverse underwater scenes, which facilitates subsequent applications.

A CSR-based visible and infrared image fusion method in low illumination conditions for sense and avoid

AbstractMachine vision has been extensively researched in the field of unmanned aerial vehicles (UAV) recently. However, the ability of Sense and Avoid (SAA) largely limited by environmental visibility, which brings hazards to flight safety in low illumination or nighttime conditions. In order to solve this critical problem, an approach of image enhancement is proposed in this paper to improve image qualities in low illumination conditions. Considering the complementarity of visible and infrared images, a visible and infrared image fusion method based on convolutional sparse representation (CSR) is a promising solution to improve the SAA ability of UAVs. Firstly, the source image is decomposed into a texture layer and structure layer since infrared images are good at characterising structural information, and visible images have richer texture information. Both the structure and the texture layers are transformed into the sparse convolutional domain through the CSR mechanism, and then CSR coefficient mapping are fused via activity level assessment. Finally, the image is synthesised through the reconstruction results of the fusion texture and structure layers. In the experimental simulation section, a series of visible and infrared registered images including aerial targets are adopted to evaluate the proposed algorithm. Experimental results demonstrates that the proposed method increases image qualities in low illumination conditions effectively and can enhance the object details, which has better performance than traditional methods.

Nested approach for X-ray image enhancement based on contrast adaptive using histogram equalization

Medical image enhancement is a topic of great interest to researchers due to the rapid evolution of technology and advancements in communication. There are many types of medical images such as X-ray images, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound images, positron emission tomography (PET) scans, single photon emission computed tomography (SPECT) scans, digital radiography images, mammography images and Fluoroscopy images. X-ray imaging is a valuable tool for diagnosis, monitoring, and treatment of many medical conditions, and its non-invasive, widely available, low cost and fast nature makes it a popular choice for many medical professionals. The proposed approach presents an algorithm for enhancing X-ray images, improving their visual appearance and making their content more useful and meaningful. The results of the algorithm show that enhanced images have a more natural look and provide accurate details of the objects in the X-ray images. Overall, this algorithm can aid in the diagnostic process by providing clearer and more detailed images for medical professionals to interpret.