Multi-Frame Adaptive Image Enhancement Algorithm for vehicle-mounted dynamic scenes

Although single-frame machine learning image restoration techniques have been shown to be effective, the proposed multi-frame approach takes advantage of both spatial and temporal information to resolve high-resolution and high-dynamic-range images. The proposed algorithm is an extension of the previously proposed algorithm DeblurGAN-C and aims to further improve the capabilities of image restoration in degraded visual environments. The main contributions of the proposed techniques include: 1) Development of an effective framework to generate a multi-frame training dataset typical of degraded visual environments; 2) Adopting a multi-frame image restoration framework that generates a single restored image as the output; 3) Conducting substantial experiments against the generated multi-frame training dataset and demonstrate the effectiveness of the proposed multi-frame image enhancement algorithm.

Environment awareness technology is one of the most critical technologies for autonomous vehicles, and traffic sign recognition is an important branch of environment awareness system, which has important research significance for ensuring traffic safety. To solve the problems of easy omission and inaccurate positioning for traffic sign detection under complex illumination conditions, there are two main innovations in this paper: firstly, an adaptive image enhancement algorithm was proposed to improve the image quality under complex illumination conditions; Secondly, a novel and lightweight attention block named Feature difference (FD) model was proposed to detect and recognize traffic signs. Unlike state-of-the-art attention models, which utilizes the difference between two feature maps to generate the attention mask. In this work, the single-stage target detection algorithm SSD was selected as the basic network, the backbone network was set as ResNet and VGG respectively and FD module was added. A large number of experiments were carried out to evaluate the optimization effect of FD module. The following conclusions can be drawn: image enhancement algorithms can provide better image samples; The FD model can be integrated into most of CNNs just by adding some shortcut connections, which does not introduce any additional parameters and layers; The average detection accuracy with FD module is 1.80% higher and the average recall rate was 1.51% higher than that without FD module, which has no great influence on the running speed. The FD module has better enhancement effect for dark image detection; The detection accuracy of the FD module is slightly better than other attention modules including BAM, CBAM and SE; The FD model can be used to evaluate the effect of the convolutional operation on features, which could help to find convolutional layers that have little effect on the accuracy of recognition for the network pruning.

Channel adaptive underwater image enhancement algorithm based on pixel level

Adaptive Fractional Order Differential Image Enhancement Algorithm Based on Image Complexity

The traffic block port monitors and manages the road traffic by shooting and recording the motor vehicles. However, due to the complex factors such as shooting angle, light condition, environmental background, etc., the recognition rate of license plate is not high enough. High light and low light under complex lighting conditions are symmetry problems. This paper analyzes and solves the low light problem in detail, an image adaptive enhancement algorithm under low light conditions is proposed in the paper. The algorithm mainly includes four modules, among which, the fast image classification module uses the deep and separable convolutional neural network to classify low-light images into low-light images by day and low-light images by night, greatly reducing the computation burden on the basis of ensuring the classification accuracy. The image enhancement module inputs the classified images into two different image enhancement algorithms and adopts the idea of dividing and ruling; the image quality evaluation module adopts a weighted comprehensive evaluation index. The final experiment shows that the comprehensive evaluation indexes are all greater than 0.83, which can improve the subsequent recognition of vehicle face and license plate.

How to capture and enhance the target from an image with complex texture and edge features is studied in this paper. For the traditional image enhancement methods can not split different kinds of the textures and edges of an image efficiently, a novel adaptive enhancement algorithm based on nonsubsampled contourlet transform (NSCT) is projected. Firstly, by using the direct spatial multiplication of NSCT coefficients of each same directional subband at adjacent highpass scales, the inter-scale spaces are constructed and the optimal geometrical directions of each matrix region are calculated in the proposed spaces. Experimental results show that, in NSCT domain, the geometrical direction information which locates in the inter-scale spaces can represent the different detail features exactly of an image. Therefore, the optimal geometrical direction information as a rule to determine what kind of edge and texture features would be captured and enhanced is employed. After comparing with existing enhancement method, the proposed approach achieves better results, not only clearly identifies target edges and textures from complex background, but also enhances them with better visualization.

This paper addresses the problem of speech enhancement and acoustic noise reduction by partial and set-membership adaptive algorithms combined with the symmetric decorrelating adaptive (SAD) algorithm structure. In this paper, we propose two new adaptive algorithms based on set-membership principle that improve the original set-membership algorithm behavior in speech enhancement applications. The first proposed algorithm (called Proposed 1), is based on the combination of the SAD algorithm structure with a smart control that uses decorrelating properties between the output and the mixing signal to control and update the SAD adaptive filters. The second proposed algorithm (called Proposed 2) is a modification of Proposed 1 and based on a new regularization relations of the SAD adaptive filters that use a combination between the variance of the mixing and the output signals of the SAD structures. These two proposed algorithms (Proposed 1 and Proposed 2) aim to improve the convergence speed performance and the output signal-to-noise-ratio of the original SAD algorithm when no smart control of the adaptive filters is used. The proposed algorithms have very interesting properties with non-stationary signal like speech when the SAD algorithm is, used alone, fails. The simulation results that are obtained by the comparison between the proposed algorithms (Proposed 1 and Proposed 2) and the original two-channel set-membership NLMS algorithm have shown the best performances of Proposed 1 and Proposed 2 in terms of the following criteria: systems mismatch, segmental SNR and segmental men square error.

A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images

In the bearing inner and outer ring surface defect detection, the traditional wavelet threshold algorithm is general effective for image processing of bearing images with insufficient illumination or uneven illumination. This paper proposes a method to improve the wavelet denoising enhancement of the threshold function. Firstly, the multilevel decomposition of bearing image is carried out by wavelet;Furthermore, adopted the improved algorithm and adaptive algorithm respectively to conduct the denoising enhancement for the first to third levels of wavelet coefficients;Finally, the wavelet coefficients and scaling coefficients are processed by wavelet inverse transform. Experiments showed that this algorithm can effectively denoise and enhance the contrast of images, processing images with good results,which is better than the traditional enhancement algorithms.

Two new fast gradient algorithms are presented employing convergence factors which are optimized in the least-squares sense, and which perform two-dimensional block adaptive filtering. These two algorithms are termed the two-dimensional optimum block algorithm with individual adaptation of parameters (TDOBAI) and the two-dimensional optimum block adaptive algorithm (TDOBA). Using computer simulation, the convergence properties of the TDOBAI and TDOBA algorithm are investigated and compared with the two-dimensional block least-mean-square (TDBLMS) algorithm which uses a convergence factor that is constant for each 2D coefficient at each block iteration. It is also shown that for the TDOBAI and TDOBA algorithms, the convergence, speed and accuracy of adaptation are greatly improved at the expense of a modest increase in computational complexity, as compared to the TDBLMS algorithm. The effectiveness of the algorithms is demonstrated in 2D system modeling, restoration (2D additive noise cancellation) and enhancement of artificially degraded images. >

Histogram equalisation has been widely used for image enhancement because of its simple implementation and satisfactory performance. However, traditional histogram equalisation uniformly redistributes an entire histogram or multiple piecewise histograms with the same equalisation strategy, which may produce unnatural artefacts, over‐enhancement or under‐enhancement in wide dynamic range dark image enhancement. This study proposes an adaptive extended piecewise histogram equalisation algorithm (AEPHE) for dark image enhancement. First, an original histogram is divided into a group of extended piecewise histograms. Then, an adaptive histogram equalisation, which balances intensity preservation and contrast boosting, is further developed and respectively applied to these extended piecewise histograms. The final histogram for image enhancement is produced by a weighted fusion of these equalised histograms. The experimental results indicate that AEPHE is superior to multiple state‐of‐the‐art algorithms.

Coal gangue separation serves as an effective method to enhance coal quality, and machine vision-based coal gangue detection methods hold significant research and application potential. Industrial dust leads to unclear imaging and affects detection accuracy. However, there is currently little research on the impact of dust. To achieve high visual detection performance of coal and gangue under dust conditions, this study researched visual characteristics and detection methods under smoke-simulated dust conditions. Firstly, we analyze the influence of smoke concentration on the coal gangue images captured by a line array camera, showing that the higher the smoke concentration, the lower the image brightness and clarity. Subsequently, we proposed an adaptive image enhancement algorithm MSRCP-Ada-BR by integrating an adaptive brightness regularization method into the MSRCP algorithm. Experimental results demonstrate that this algorithm significantly enhances the brightness and clarity of coal gangue images across varying smoke concentrations, aligning the pixel distribution of the enhanced images more closely with that of smoke-free images. Ultimately, by integrating the MSRCP-Ada-BR into the input of YOLOx network, we construct a coal gangue detection model suitable for dusty environments. Compared to the original YOLOx model, the detection model integrated with MSRCP-Ada-BR achieves a 5.59% improvement in accuracy.

A numerical method for image reconstruction using electromagnetic diffraction tomography is presented. An adaptive algorithm is proposed for the reconstruction of the complex permittivity of an inhomogeneous lossy dielectric object having arbitrary shape. Starting from integral representations of the scattered electric field and using the moment method, an algorithm based on LMS is applied to the two dimensional problem. Some results of the simulated data are presented. Finally the authors show that successful results can be obtained with this adaptive reconstruction algorithm. >

The paper presents a new Adaptive Gain calculating approach when using the Adaptive Image Enhancement algorithm based on Wavelet Transform. The basic technique is to select two different thresholds which divide the input into three parts after the wavelet coefficients are normalized. For the wavelet coefficients less than the smaller threshold, just make the output zero. And this provides a de-noise effect. The output remains unchanged when the wavelet coefficients are greater than the larger one. For the last part, find a function to make the output figure S shape. So the algorithm can give a clear contrast and the function is the key. The final goal is to use this method in the process of Online Vision Measure, we have chosen the TI TMS320 DM642 Digital Signal Processor or DSP because of its powerful multimedia processing capability. What's more, the TI corporation has provided a variety of software develop libraries as well as the 3rd party tools. All such tools make the development more rapid and convenient. After the technique this paper provides is implemented on DSP, a series of optimization will be performed to make it suitable for industry real-time usage.

There are limited dynamic range problems when real world scenes are captured from a digital camera and displayed on monitors.Retinex is one of the most popular theories for dynamic range compression, color constancy and color rendition.However, there still exist some problems, such as hue shift and excessive noise, especially for those images including large bright areas and dark regions.In order to solve these problems,an adaptive weight value-based multi-scale retinex algorithm is proposed in this paper. Compared to other methods, visual experiments and data evaluation show that the proposed algorithms can achieve better performance for color image enhancement and noise suppression simultaneously. What is more, there is no need for color correction phase in the proposed algorithm, which makes it more efficient and convenient to apply.