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Cycle-consistent Adversarial Network (Cycle GAN) is an image generation model based on a Generative Adversarial Network (GAN), which has been widely used. In this paper, an improved image enhancement network based on Cycle GAN is proposed for low-dose CT image enhancement. By introducing a new loss function and network structure, the network improves the effect of image enhancement. In order to reduce the damage of X-rays to the human body, Low-dose CT (LDCT) is widely used in clinical diagnosis. However, quantum noise will be introduced into the projection data while the radiation dose is reduced. The quality of reconstructed CT images is reduced. Aiming at the problem of image noise in low-dose CT, the traditional image noise reduction methods cannot meet the practical needs due to the complicated process of noise reduction or the defects of the algorithm itself. A network model based on the residuals module is established, three identical residuals blocks are added to the original network, and the residual function after adding residuals blocks is optimized. In the part of generating network, deconvolution is used instead of reshaping the enhanced image, and the improved network is more stable in performance. The validity of the network model is verified by the test data.

With the rapid evolution of digital technology, graphic design has become increasingly pivotal across various domains. While traditional image enhancement methods have addressed issues in texture boundaries and information retrieval, they often neglect challenges posed by noise in graphic design, leading to uneven enhancements. Therefore, this study proposes a multi-scale detail enhancement method to improve the visual perception quality of graphic design images. Nonlinear transformation is applied to the image to obtain a preliminary enhanced image. Subsequently, both the preliminary enhanced image and the low brightness image are simultaneously fed into a multi-scale feature extraction block for feature extraction. In order to improve the ability of online learning of semantic features, a U-shaped feature enhancement module is introduced in each scale feature extraction branch, which increases the feature extraction of contextual information. Finally, the enhanced image is obtained by integrating multi-scale feature information. The experimental results show that this method is relatively superior in terms of visual effects and metrics, and significantly improves color restoration, texture preservation, and detail enhancement, providing a promising direction for image enhancement in graphic design.

The main environmental factors that interfere with asphalt pavement crack detection include shadows from ambient light of different intensities, trees, signboards, railings, etc. Traditional crack detection methods usually eliminate the effect of light during pre-processing or improve the recognition results by local consistency and light normalization during image segmentation. However, the current methods can only improve the image brightness uniformity and cannot completely eliminate the effect of light, so the usual methods are only effective when the image brightness uniformity is good. So, this study puts out an approach to improving images. Machine learning is used to suggest a method for detecting cracks in asphalt pavement, with the help of an attention mechanism, using photos of real roads as the experimental dataset. Experiments show that the method can preprocess image data well and enhance the robustness of training in machine learning structures. Test results show that our method can be well applied to practical testing work.

This paper first proposes the notion of the intuitionistic fuzzy sets on inclusion degree, furthermore, a couple of dual operators’ lower approximations, and upper approximations of fuzzy inclusion approximate space are provided, thus, a probabilistic intuitionistic fuzzy set model that stemmed from inclusion degree was obtained. A rough intuitionistic fuzzy set and histogram equalization-based image enhancement algorithm is proposed to address the shortcomings of excessive enhancement and loss of image detail information in fuzzy enhancement that cannot improve image contrast and histogram equalization enhancement. The fusion of rough intuitionistic blur enhancement and histogram equalization focuses on rough intuitionistic blur enhancement while suppressing the shortcomings of histogram equalization, which not only enhances the detailed information of the image but also improves its contrast. Finally, its effectiveness is verified through typical image enhancement examples.

Medial images are contaminated by multiplicative speckle noise, which dramatically reduces ultrasound images and has a detrimental impact on a variety of image interpretation tasks. Hence, to overcome this issue, this paper presented a Two-Phase Speckle Reduction approach with Improved Anisotropic Diffusion and Optimal Bayes Threshold termed TPSR-IADOT, which includes the phases like image enhancement and two-level decomposition processes. Initially, the speckle noise is subjected to an image enhancement process where the Speckle Reducing Improved Anisotropic Diffusion (SRAID) filtering process is carried out for the speckle removal process. Afterwards, two-level decomposition takes place which utilizes Discrete Wavelet Transform (DWT) to remove the residual noise. As the speckle noise is mostly present in the high-frequency band, Improved Bayes Threshold will be applied to the high- frequency subbands. Finally, to provide the best outcomes, an optimization algorithm termed Self Improved Pelican Optimization Algorithm (SI-POA) in this work via choosing the optimal threshold value. The efficiency of the proposed method has been validated on an ultrasound image database using Simulink in terms of PSNR, SSIM, SDME and MAPE. Accordingly, from the analysis, it is proved that the proposed TPSR-IADOT attains the PSNR of 40.074, whereas the POA is 38.572, COOT is 38.572, BES is 37.003, PRO is 30.419, WOA is 33.218, RFU-LA is 29.935 and SSI-COA is 39.256, for noise variance$=$0.1.

In medical science, appropriate monitoring of the function of the human body, physical representations, and accurate disease is a complex task. It helps in diagnosing and treating diseases by revealing internal structures hidden by the skin and bones. Image enhancement techniques play a crucial role in medical imaging, as they improve diagnostic accuracy and visualization. However, the conventional methods were limited by noise sensitivity, lack of adaptability, and computational complexity. Therefore, this paper proposes One-to-One Honey Optimization (OOHO) for medical image enhancement. At first, the input brain Magnetic Resonance Imaging (MRI) image is processed to find the histogram. After that, histogram clipping is performed based on the clipping threshold. Then, the exposure threshold is computed to create sub-histograms. Moreover, the Probability Density Function (PDF) is computed and updated. Likewise, the Cumulative Density Function (CDF) and the mapping function are computed in each sub-histogram. Therefore, the finest enhanced image is obtained by integrating all sub-images. The fitness value is computed utilizing the cost function. Furthermore, the optimal threshold is accomplished by utilizing OOHO, which is developed by the integration of a One-to-One-based Optimizer (OOBO) and Honey Badger optimization (HBO). The evaluation results show that the OOHO attained a Degree of Distortion (DD) of 0.270, a Pear Signal to Noise Ratio (PSNR) of 47.060 dB, a Mean Square Error (MSE) of 0.570, Structural Similarity Index Measure (SSIM) as 0.960, and Root Mean Square Error (RMSE) of 0.755. The high performance is noted by the devised model and the MSE of the OOBO, HBO, hybrid Simulated Annealing-Evaporation Rate-based Water Cycle (SA-ERWCA) algorithm, Particle Swarm Optimization combined with Histogram Equalization (PSO-HE), Genetic Algorithm-based Adaptive Histogram Equalization (GAAHE), High-Quality Guidance Network (HQG-Net), multi-scale attention generative adversarial network (MAGAN), and Gaussian Quantum-behaved Arithmetic Optimization Algorithm (GQAOA) methods are 0.657, 0.637, 0.637, 0.627, 0.670, 0.650, 0.650, and 0.640, respectively.

Advances in digital technologies have allowed us to generate more images than ever. Images of scanned documents are examples of these images that form a vital part in digital libraries and archives. Scanned degraded documents contain background noise and varying contrast and illumination, therefore, document image binarisation must be performed in order to separate foreground from background layers. Image binarisation is performed using either local adaptive thresholding or global thresholding; with local thresholding being generally considered as more successful. This paper presents a novel method to global thresholding, where a neural network is trained using local threshold values of an image in order to determine an optimum global threshold value which is used to binarise the whole image. The proposed method is compared with five local thresholding methods, and the experimental results indicate that our method is computationally cost-effective and capable of binarising scanned degraded documents with superior results.

This paper describes the implementation of a parallel image processing algorithm, the aim of which is to give good contrast enhancement in real time, especially on the boundaries of an object of interest defined by a grey homogeneity (for example, an object of medical interest having a functional or morphologic homogeneity, like a bone or tumor). The implementation of a neural network algorithm which does this contrast enhancement has been done on a SIMD massively parallel machine (a MasPar of 8192 processors) and the communication between its processors has been optimized.

In order to restore image color and enhance contrast of remote sensing image without suffering from color cast and insufficient detail enhancement, a novel improved multi-scale retinex with color restoration (MSRCR) image enhancement algorithm based on Gaussian filtering and guided filtering was proposed in this paper. Firstly, multi-scale Gaussian filtering functions were used to deal with the original image to obtain the rough illumination components. Secondly, accurate illumination components were acquired by using the guided filtering functions. Then, combining with four-direction Sobel edge detector, a self-adaptive weight selection nonlinear image enhancement was carried out. Finally, a series of evaluate metrics such as mean, MSE, PSNR, contrast and information entropy were used to assess the enhancement algorithm. The results showed that the proposed algorithm can suppress effectively noise interference, enhance the image quality and restore image color effectively.

The infrared traffic image acquired by the intelligent traffic surveillance equipment has low contrast, little hierarchical differences in perceptions of image and the blurred vision effect. Therefore, infrared traffic image enhancement, being an indispensable key step, is applied to nearly all infrared imaging based traffic engineering applications. In this paper, we propose an infrared traffic image enhancement algorithm that is based on dark channel prior and gamma correction. In existing research dark channel prior, known as a famous image dehazing method, here is used to do infrared image enhancement for the first time. Initially, in the proposed algorithm, the original degraded infrared traffic image is transformed with dark channel prior as the initial enhanced result. A further adjustment based on the gamma curve is needed because initial enhanced result has lower brightness. Comprehensive validation experiments reveal that the proposed algorithm outperforms the current state-of-the-art algorithms.

Images taken in poor environmental conditions decrease the visibility and hidden information of digital images. Therefore, image enhancement techniques are necessary for improving the significant details of these images. An extensive review has shown that histogram-based enhancement techniques greatly suffer from over/under enhancement issues. Fuzzy-based enhancement techniques suffer from over/under saturated pixels problems. In this paper, a novel Type-II fuzzy-based image enhancement technique has been proposed for improving the visibility of images. The Type-II fuzzy logic can automatically extract the local atmospheric light and roughly eliminate the atmospheric veil in local detail enhancement. The proposed technique has been evaluated on 10 well-known weather degraded color images and is also compared with four well-known existing image enhancement techniques. The experimental results reveal that the proposed technique outperforms others regarding visible edge ratio, color gradients and number of saturated pixels.

Face recognition is a vastly researched topic in the field of computer vision. A lot of work have been done for facial recognition in two dimensions and three dimensions. The amount of work done with face recognition invariant of image processing attacks is very limited. This paper presents a total of three classes of image processing attacks on face recognition system, namely image enhancement attacks, geometric attacks and the image noise attacks. The well-known machine learning techniques have been used to train and test the face recognition system using two different databases namely Bosphorus Database and University of Milano Bicocca three-dimensional (3D) Face Database (UMBDB). Three classes of classification models, namely discriminant analysis, support vector machine and k-nearest neighbor along with ensemble techniques have been implemented. The significance of machine learning techniques has been mentioned. The visual verification has been done with multiple image processing attacks.

A kriging method is presented as a spatial filter for smoothing gray-scale images degraded by Gaussian white noise. The concepts are based on the analysis of semivariances, the linear combination scheme of kriging, and fuzzy sets. Application of fuzzy sets allows a gradual transition between two boundaries of semivariance levels as a criterion for smoothing the pixel values. This fuzzy thresholding also allows some degree of flexibility to suit various desired results for particular problems. Experimental results obtained by the fuzzy kriging filter are smoother and still preserve edges compared with those by the adaptive Wiener filter.

For image enhancement method based on the fractional order differential, it is difficult to artificially give the optimal order of the fractional differential which can make the image enhancement effect better, and it is hard to ensure the enhancement of the target object while preserving the information of background pixels if the entire image is filtered by a fixed differential order. In order to solve this problem, the image is segmented into the object area and the background area according to the Otsu threshold algorithm based on Markov Random Field firstly. On the basis of the principle of the fractional differential for image enhancement, a piecewise function is established by combining with the different characteristics of pixels in each area, then the best order of fractional differential in the two areas can be determined adaptively. Thus, a novel adaptive fractional order differential algorithm for image enhancement on the basis of segmentation is put forward. Several fog-degraded traffic images are selected for experiments and processed by three other algorithms. The results of comparison exhibit the superiority of our algorithm.

To improve contrast and restore color for underwater images without suffering from insufficient details and color cast, this paper proposes a fusion algorithm for different color spaces based on contrast limited adaptive histogram equalization (CLAHE). The original color image is first converted from RGB space to two different spaces: YIQ and HSI. Then, the algorithm separately applies CLAHE in YIQ and HSI color spaces to obtain two different enhanced images. After that, the YIQ and HSI enhanced images are respectively converted back to RGB space. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, using a 4-direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse the YIQ-RGB and HSI-RGB images together to achieve the final image. The experimental results showed that the proposed algorithm provided more detail enhancement and higher values of color restoration than other image enhancement algorithms. The proposed algorithm can effectively reduce noise interference and observably improve the image quality for underwater images.

In recent years, the fractional order derivative has been introduced for image enhancement. It was proved that the medical image enhancement method based on the fractional order derivative has better effect than the method based on the integral order calculus. However, a priori information such as texture surrounding a pixel is normally ignored by the traditional fractional differential operators with the same value in the eight directions. To address the above problem, this paper presents a new medical image enhancement method by taking the merits of fractional differential and directional derivative. The proposed method considers the surrounding information (such as the image edge, clarity and texture information) and structural features of different pixels, as well as the directional derivative of each pixel in constructing the masks. By proposing this method, it can not only improve the high frequency information, but also improve the low frequency information of the image. Ultimately, it enhances the texture information of the image. Extensive experiments on four kinds of medical image demonstrate that the proposed algorithm is in favor of preserving more texture details and superior to the existing fractional differential algorithms on medical image enhancement.

A depth map and a full focus image can be obtained by using the image sequence and the image evaluation function. The depth map obtained by gradient operator as the evaluation function has good resolution but is also affected by the deviation value. This kind of noise is different from common salt-and-pepper noise and Gaussian noise, so the median filter or mean filter cannot play a very good role. In order to improve the effect of 3D modeling, it is necessary to eliminate the deviation and retain and highlight the depth information. In this paper, an adaptive image enhancement method based on mode cooperative filtering is proposed. This method uses the general level of mode data to express the data, and uses the method of cooperative filtering to form the centralized balance and isolated deviation around the mode. Compared with other filters, the results show that it can achieve better results.

Optical properties of water distort the quality of underwater images. Underwater images are characterized by poor contrast, color cast, noise and haze. These images need to be pre-processed so as to get some information. In this paper, a novel technique named Fusion of Underwater Image Enhancement and Restoration (FUIER) has been proposed which enhances as well as restores underwater images with a target to act on all major issues in underwater images, i.e. color cast removal, contrast enhancement and dehazing. It generates two versions of the single input image and these two versions are fused using Laplacian pyramid-based fusion to get the enhanced image. The proposed method works efficiently for all types of underwater images captured in different conditions (turbidity, depth, salinity, etc.). Results obtained using the proposed method are better than those for state-of-the-art methods.

Due to the scattering and absorption effects in the undersea environment, underwater image enhancement is a challenging problem. To obtain the ground-truth data for training is also an open problem. So, the learning process is unavailable. In this paper, we propose a Low-Rank Nonnegative Matrix Factorization (LR-NMF) method, which only uses the degraded underwater image as input to generate the more clear and realistic image. According to the underwater image formation model, the degraded underwater image could be separated into three parts, the directed component, the back and forward scattering components. The latter two parts can be considered as scattering. The directed component is constrained to have a low rank. After that, the restored underwater image is obtained. The quantitative and qualitative analyses illustrate that the proposed method performed equivalent or better than the state-of-the-art methods. Yet, it’s simple to implement without the training process.

In this paper, we propose a structure-preserving and denoising low-light enhancement method that uses the coefficient of variation. First, we use the coefficient of variation to process the original low-light image, which is used to obtain the enhanced illumination gradient reference map. Second, we use the total variation (TV) norm to regularize the reflectance gradient, which is used to maintain the smoothness of the image and eliminate the artifacts in the reflectance estimation. Finally, we combine the above two constraint terms with the Retinex theory, which contains the denoising regular term. The final enhanced and denoised low-light image is obtained by iterative solution. Experimental results show that our method can achieve superior performance in both subjective and objective assessments compared with other state-of-the-art methods (the source code is available at: https://github.com/bbxavi/SPDLEM.).