Detail Enhancement Algorithm Research Articles

Retraction Note: Computer-aided visual communication design based on image detail enhancement algorithm

Retraction Note: Computer-aided visual communication design based on image detail enhancement algorithm

Retraction Note: Application of dynamic simulation technology based on image detail enhancement algorithm in urban 3D landscape

Retraction Note: Application of dynamic simulation technology based on image detail enhancement algorithm in urban 3D landscape

RETRACTED ARTICLE: Application of dynamic simulation technology based on image detail enhancement algorithm in urban 3D landscape

RETRACTED ARTICLE: Application of dynamic simulation technology based on image detail enhancement algorithm in urban 3D landscape

RETRACTED ARTICLE: Computer-aided visual communication design based on image detail enhancement algorithm

RETRACTED ARTICLE: Computer-aided visual communication design based on image detail enhancement algorithm

Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

Cervical cancer is a prevalent chronic malignant tumor in gynecology, necessitating high-quality images of cervical precancerous lesions to enhance detection rates. Addressing the challenges of low contrast, uneven illumination, and indistinct lesion details in such images, this paper proposes an enhancement algorithm based on retinex and histogram equalization. First, the algorithm solves the color deviation problem by modifying the quantization formula of retinex theory. Then, the contrast-limited adaptive histogram equalization algorithm is selectively conducted on blue and green channels to avoid the problem of image visual quality reduction caused by drastic darkening of local dark areas. Next, a multi-scale detail enhancement algorithm is used to further sharpen the details. Finally, the problem of noise amplification and image distortion in the process of enhancement is alleviated by dynamic weighted fusion. The experimental results confirm the effectiveness of the proposed algorithm in optimizing brightness, enhancing contrast, sharpening details, and suppressing noise in cervical precancerous lesion images. The proposed algorithm has shown superior performance compared to other traditional methods based on objective indicators such as peak signal-to-noise ratio, detail-variance–background-variance, gray square mean deviation, contrast improvement index, and enhancement quality index.

Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory

To address the issues of fuzzy scene details, reduced definition, and poor visibility in images captured under non-uniform lighting conditions, this paper presents an algorithm for effectively enhancing such images. Firstly, an adaptive color balance method is employed to address the color differences in low-light images, ensuring a more uniform color distribution and yielding a low-light image with improved color consistency. Subsequently, the image obtained is transformed from the RGB space to the HSV space, wherein the multi-scale Gaussian function is utilized in conjunction with the Retinex theory to accurately extract the lighting components and reflection components. To further enhance the image quality, the lighting components are categorized into high-light areas and low-light areas based on their pixel mean values. The low-light areas undergo improvement through an enhanced adaptive gamma correction algorithm, while the high-light areas are enhanced using the Weber–Fechner law for optimal results. Then, each block area of the image is weighted and fused, leading to its conversion back to the RGB space. And a multi-scale detail enhancement algorithm is utilized to further enhance image details. Through comprehensive experiments comparing various methods based on subjective visual perception and objective quality metrics, the algorithm proposed in this paper convincingly demonstrates its ability to effectively enhance the brightness of non-uniformly illuminated areas. Moreover, the algorithm successfully retains details in high-light regions while minimizing the impact of non-uniform illumination on the overall image quality.

Image detail enhancement of nanocomposites based on deep convolution neural networks

Research on the image detail enhancement algorithm of nanocomposites can better improve the image quality of nanocomposites and facilitate researchers to understand the structural characteristics of materials. Due to the problems of low image quality and difficulty to retain the details of traditional nanocomposite image details enhancement algorithms, a new nanocomposite image details enhancement algorithm based on deep convolution neural networks (DCNN) is designed. Through histogram equalization, linear gray-scale stretching and median filtering, the image of nanocomposite is preprocessed, and then FCM algorithm is used for edge detection and image segmentation. Using color model transformation algorithm and DCNN, an image detail enhancement model including feature extraction and nonlinear mapping is constructed. Finally, the idea of image detail reconstruction is introduced, and image detail reconstruction is realized through a convolution layer. The results show that compared with the experimental comparison algorithm, the image processed by the proposed algorithm contains more detailed information and has higher image quality. The PSNR value reaches 27.1 dB, which indicates that the nanocomposite image has a better detail enhancement effect and can be widely used in the field of nanocomposite analysis and research.

Intensity mixture and band-adaptive detail fusion for pansharpening

Pansharpening aims to sharpen a low-resolution multispectral (MS) image through a high-resolution single-channel panchromatic (PAN) image to obtain a high-resolution multi-spectral (HRMS) image. However, low correlation between the PAN and MS images, as well as the inaccurate detail injection for each band of MS image are the key problems causing spectral and spatial distortions in pansharpening. To address these issues, a new pansharpening method based on the intensity mixture and band-adaptive detail fusion is proposed. To obtain a mixed-intensity image (T) that has a high correlation with the MS image and maintain the gradient information of the PAN image, the intensity mixture model is constructed by establishing the intensity and gradient constraints between T and the source images. As it is hard to obtain a proper degradation filter in the model, a filter estimation algorithm is designed by the distribution alignment. To inject the details that match the point spread function of the sensor, a band-adaptive detail fusion algorithm is presented to fuse the details extracted from T with those from the MS image for each band. Furthermore, as there are far fewer details in the MS image than in T, a detail enhancement algorithm is proposed to enhance the details proportionally. The final HRMS image is obtained by injecting the fused details into the upsampled MS image. Extensive experiments show that the proposed method can efficiently achieve the best results in fusion quality compared to state-of-the-art methods. The code is availabe at https://github.com/yotick/IMBD.

Retracted: Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury.

[This retracts the article DOI: 10.1155/2021/7381466.].

Acceleration method of infrared image detail enhancement on FPGA

Among the many cockpit human-computer interaction systems of airborne, vehicle-mounted, and ship-borne platforms, the image detail enhancement technology can improve the ability of personnel to interpret infrared images, which has very important application requirements. The enhanced algorithm running on the embedded computing platform needs to have a higher processing speed and smaller time delay in meeting the needs of real-time interaction. The current infrared video sensor usually has a lower resolution, and the image detail enhancement algorithm still can achieve real-time processing performance on a homogeneous multi-core CPU processing platform. However, as the resolution of platform sensors continues to increase, image processing speed and time delay are difficult to meet application requirements. We propose a method for enhancing and accelerating infrared image details for FPGA platforms. By considering the FPGA's specific domain software and hardware computing architecture, the traditional bilateral filtering image details algorithm is accelerated using local cache and search table, so that real-time processing performance can still be achieved under the input image of the 4k resolution, while almost no additional memory access delay is added. While ensuring the processing effect, the algorithm greatly improves the processing and delay indicators to meet the application requirements of embedded equipment in specific fields such as the cockpit human-computer interaction system.

(Retracted) Infrared image filtering and enhancement processing method based upon image processing technology

Due to the processing method used in many existing image enhancement algorithms, some inherent noise in the image is amplified when the image is enhanced globally or locally. The quality of infrared images directly affects the wide application of infrared imaging technology, yet the quality of infrared image depends largely on the advanced nature and correct application of infrared image processing technology (IPT). This research mainly discusses the infrared image filtering enhancement processing method based on IPT. We briefly summarize the research significance, application fields and research status of the image registration and enhancement. We summarize the commonly used methods. We use a contrast saliency filter to obtain the region of interest and perform statistical segmentation on the region to make it more suitable for observation and subsequent processing. No matter what the background environment is, the image detail enhancement algorithm enhances all the details in the image in a balanced manner. Therefore, we adopt the scheme of separating the background and the details to avoid the influence of the background difference on the detail display effect in other enhancement methods. Objective experiments have also proved that it is difficult to achieve consistency between subjective and objective evaluation of image quality. Therefore, the evaluation of processed image quality still needs to combine subjective visual effects and objective evaluation. We use optimized hierarchical differential expression theory to solve the acquired saliency region and obtain the differential vector that realizes the gray difference amplification of this region. The dynamic range of the original image is very narrow, roughly concentrated in [0, 50]. The gray range of the image processed by the enhancement algorithm has been well expanded, basically covering [0, 255]. The proposed infrared image enhancement method achieves a better visual enhancement effect.

Multi-exposure microscopic image fusion-based detail enhancement algorithm

Traditional microscope imaging techniques are unable to retrieve the complete dynamic range of a diatom species with complex silica-based cell walls and multi-scale patterns. In order to extract details from the diatom, multi-exposure images are captured at variable exposure settings using microscopy techniques. A recent innovation shows that image fusion overcomes the limitations of standard digital cameras to capture details from high dynamic range scene or specimen photographed using microscopy imaging techniques. In this paper, we present a cell-region sensitive exposure fusion (CS-EF) approach to produce well-exposed fused images that can be presented directly on conventional display devices. The ambition is to preserve details in poorly and brightly illuminated regions of 3-D transparent diatom shells. The aforesaid objective is achieved by taking into account local information measures, which select well-exposed regions across input exposures. In addition, a modified histogram equalization is introduced to improve uniformity of input multi-exposure image prior to fusion. Quantitative and qualitative assessment of proposed fusion results reveal better performance than several state-of-the-art algorithms that substantiate the method’s validity.

Edge detail enhancement algorithm for high-dynamic range images

Abstract Existing image enhancement methods have problems of a slow data transmission and poor conversion effect, resulting in a low image-recognition rate and recognition efficiency. To solve these problems and improve the recognition accuracy and recognition efficiency of image features, this study proposes an edge detail enhancement algorithm for a high-dynamic range image. The original image is transformed by Fourier transform, and the low-frequency and high-frequency images are obtained by the frequency-domain Gaussian filtering and inverse Fourier transform. The low-frequency image is processed by the contrast limited adaptive histogram equalization, and the high-frequency image is obtained by the nonsharpening masking and gray transformation. The low-frequency enhanced and the high-frequency enhanced images are weighted and fused to enhance the edge details of the image. Finally, the experimental results show that the proposed high-dynamic range image edge detail enhancement algorithm maintains the image recognition rate of more than 80% during the practical application, and the recognition time is within 1,200 min, which enhances the image effect, improves the recognition accuracy and recognition efficiency of image characteristics, and fully meets the research requirements.

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior.

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.

Research on Image Enhancement Algorithm Based on Artificial Intelligence

With the continuous development of social science and technology, people have higher and higher requirements for image quality. This paper integrates artificial intelligence technology and proposes a low-illuminance panoramic image enhancement algorithm based on simulated multi-exposure fusion. First, the image information content is used as a metric to estimate the optimal exposure rate, and the brightness mapping function is used to enhance the V component, and the low-illuminance. The image and the overexposed image are input, the medium exposure image is synthesized by the exposure interpolation method, and the low illumination image, the medium exposure image and the overexposure image are merged using a multi-scale fusion strategy to obtain the fused image, which is corrected by a multi-scale detail enhancement algorithm. After the fusion, the details are enhanced to obtain the final enhanced image. Practice has proved that the algorithm can effectively improve the image quality.

Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury.

Medical imaging modalities, such as magnetic resonance imaging (MRI) and computerized tomography (CT), have allowed medical researchers and clinicians to examine the structural and functional features of the human body, thereby assisting the clinical diagnosis. However, due to the highly controlled imaging environment, the imaging process often creates noise, which seriously affects the analysis of the medical images. In this study, a medical imaging enhancement algorithm is presented for ankle joint talar osteochondral injury. The gradient operator is used to transform the image into the gradient domain, and fuzzy entropy is employed to replace the gradient to determine the diffusion coefficient of the gradient field. The differential operator is used to discretize the image, and a partial differential enhancement model is constructed to achieve image detail enhancement. Three objective evaluation indexes, namely, signal-to-noise ratio (SNR), information entropy (IE), and edge protection index (EPI), were employed to evaluate the image enhancement capability of the proposed algorithm. Experimental results show that the algorithm can better suppress noise while enhancing image details. Compared with the original image, the histogram of the transformed image is more uniform and flat and the gray level is clearer.

Learning in-place residual homogeneity for single image detail enhancement

An image detail enhancement algorithm is proposed based on in-place residual homogeneity (IP). Residual homogeneity is a physical law, which mainly explains the texture similarity between the same image residual at slightly different resolutions. As we all know, a single image can be divided into a base layer and a detail layer, and the effective estimation of the detail layer is the key in a detail enhancement algorithm. In the experiment, we find that the residual layer of an image obtained by bilinear interpolation is closely related to its detail layer, hence it can be used as the initial estimation of the detail layer, then residual homogeneity is applied to update the residual layer until the accurate detail layer is acquired. In the process of updating residuals, a searching method called fast in-place searching (FIPS) is used. FIPS only takes advantage of the residual homogeneity within the in-place region, which accelerates the project about 93%. Different from the local-based and global-based methods, our IP gets the detail layer directly and amplifies it. It has many good properties, such as being fast, edge-aware, robust, and parameter-free. Good performance has been demonstrated on several widely used datasets by both subjective and objective evaluations.

Dynamic Range Compression and Detail Enhancement Algorithm Combined with PE for High Dynamic Range Infrared Images

To address the problem of noise suppression and contrast enhancement in enhancing the details of potential or small targets in infrared images with a high dynamic range,a dynamic range and detail enhancement algorithm based on a guided filter image is proposed.The base layer is compressed by a plateau-histogram equalization algorithm.The detail layer is denoised by a median filter first,and then the potential and small target details are enhanced by nonlinear mapping.Finally,the image is integrated according to a certain weight to obtain the enhanced details.Based on the subjective and objective experimental results,compared with algorithms based on mapping,histogram equalization,and a bilateral filter,this algorithm can improve the contrast of the target scene of the infrared image in the process of dynamic range reduction.The proposed algorithm can highlight the texture features and obtain a good detail enhancement effect.

Blurred Detail Enhancement Algorithm for Motion Video Image Based on Computer Network

In this work, a blurred detail enhancement algorithm is proposed to improve the feature recognition ability of motion video image based on computer network. The edge contour features of motion video images are decomposed using multi-scale Retinex algorithm, and the image recognition technology based on computer network is used to simulate the human visual features to enhance and repair the overlay or blurred features contained in the images. The feature extraction and feature focusing of blurred detail of motion video image are realized by computer vision template feature matching method, respectively. On this basis, the enhancement effect of blurred detail image is achieved. The proposed method is proved to be able to can improve the output peak signal to noise ratio (PSNR) the blur detail quality, and the recognition ability of motion video images.

Multi-Feature Fusion Algorithm in VR Panoramic Image Detail Enhancement Processing

VR panoramic image is an image technology that covers a wide range of scenes. Its imaging range is much larger than that of traditional imaging systems, and it can fully reflect all the information of the imaging space. Although the multi-feature fusion method has been studied for a long time, the methods of multi-feature extraction, fusion and overall optimization have not been widely studied. In view of the shadow problem in VR panoramic images, this paper proposes a multi-feature fusion VR panoramic image shadow elimination algorithm, which uses HSV color features and LBP (Local Binary Pattern) / LSFP (Local Five Similarity Pattern) texture features to obtain shadow detection results and then obtains the final detection results by fusion. The experimental results prove that while ensuring a low missed detection rate, the false detection rate is greatly reduced. The comprehensive evaluation index Avg in this paper is improved by 3.4% compared with the shadow elimination algorithm based on a single feature. This paper proposes an image saliency detection algorithm and image detail enhancement algorithm based on multi-feature fusion. The final saliency map is obtained through linear fusion. Experiments prove that the image detail enhancement algorithm based on multi-feature fusion mentioned in this paper has achieved excellent results. In this paper, the performance of single feature fusion algorithm and multi-feature fusion algorithm are compared. The results show that the accuracy rate of multi-feature fusion algorithm based on HSV, LBP and LSFP is 93.39%, and the effect of multi-feature fusion is better than that of single feature.