Image denoising plays an important role in image processing, which aims to separate clean images from noisy images. A number of methods have been presented to deal with this practical problem over the past several years. The best currently available wavelet-based denoising methods take advantage of the merits of the wavelet transform. Most of these methods, however, still have difficulties in defining the threshold parameter which can limit their capability. In this paper, we propose a novel wavelet denoising approach based on unsupervised learning model. The approach taken aims at exploiting the merits of the wavelet transform: sparsity, multi-resolution structure, and similarity with the human visual system, to adapt an unsupervised dictionary learning algorithm for creating a dictionary devoted to noise reduction. Using the K-Singular Value Decomposition (K-SVD) algorithm, we obtain an adaptive dictionary by learning over the wavelet decomposition of the noisy image. Experimental results on benchmark test images show that our proposed method achieves very competitive denoising performance and outperforms state-of-the-art denoising methods, especially in the peak signal to noise ratio (PSNR), the structural similarity (SSIM) index, and visual effects with different noise levels.

/pdf/a-wavelet-denoising-approach-based-on-unsupervised-learning-28bixcxid7.pdf

A wavelet denoising approach based on unsupervised learning model

Electricity is a major source of energy for fast growing population and the use of nonrenewable source is harmful for our environment. This reason belongs to devastating of environment, so it is required to take immediate action to solve these problems which result the solar energy development. Production of a solar energy can be maximizing if we use solar follower. The major part of solar panels is microcontroller with arrangement of LDR sensor is used to follow the sun, where the sensors is less efficient to track the sun because of the low sensitivity of LDR. We are proposing a method to track sun more effetely with the help of both LDR sensors and image processing. This type of mechanism can track sun with the help of image processing software which combines both result of sensors and processed sun image to control the solar panel. The combination of both software and hardware can control thousands of solar panels in solar power plants.

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/21308/13488

Vision based solar tracking system for efficient energy harvesting

Medical imaging means the methods and procedures used for creating pictures of various parts of the human body for numerous clinical objectives. These images are constantly gets dirtied by noise during picture acquisition and transmission, resulting in low quality images. Noise is the unwanted signal which corrupts the important and desirable information. The noises can be categorized into different types based on their nature and origin. e.g. Gaussian, the impulsive and speckle noise etc. The removal of noise is very necessary for proper analysis and diagnosis. Filtering noise helps to recreate a high-quality image in digital image processing for further image processing such as segmentation of images, identification, recognition and monitoring, etc. There are various approaches to denoise medical images based on transform approach, machine learning, filtering method and statistical method. These techniques or approaches is subject to noise type exist in the image. To evaluate the denoising performance, parameters like SNR, PSNR etc. are used. This paper takes a review of current denoising techniques.

https://ijonest.net/index.php/ijonest/article/download/76/pdf

Medical Image Denoising Techniques: A Review

A Survey of Computer Vision Based Pedestrian Detection for Driver Assistance Systems

Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter. The proposed method consists of two parts within a closed loop: the first one is to estimate the system state based on the second-order adaptive statistics model and the other is to update the adaptive parameter in the model using the Yule–Walker algorithm. Specifically, the state estimation process was implemented via the Kalman filter in a recursive way, and the online purpose was therefore attained. Experimental data in a reinforced concrete structure test was used to verify the effectiveness of the proposed method. Results show the proposed method not only dealt with the signals with colored noise, but also achieved a tradeoff between efficiency and accuracy.

https://www.mdpi.com/1424-8220/17/7/1668/pdf

Online Denoising Based on the Second-Order Adaptive Statistics Model

Image processing denoising is a very important step, the hot issue is also the current research. Wavelet transform multi-resolution is frequency analysis features, in order to further improve the image denoising quality, improve visual effect, the paper image denoising methods based on wavelet transform, and which points out the further researching directions.

Image denoising based on wavelet transform

This paper proposed an improved threshold function and threshold estimation method for analyzing the characteristics of the wavelet threshold denoising. Because hard threshold denoising makes boundaries fuzzy and soft threshold denoising has Gibbs phenomenon, the new threshold function and estimation method have better adaptive. Results show that the improved method can effectively remove the white noise, the improved method is better than the soft, hard threshold denoising.

http://ieeexplore.ieee.org/iel7/7324165/7338993/07339060.pdf

Robust image denoising with an improved wavelet threshold method

The image features play an important role in object detection and recognition, the method of object detection based on Histogram of Oriented Gradient(HOG) for its good detection result receives extensive attention to many researchers. This article describes the approaches to Histogram of Oriented Gradient and support vector machine, focusing on studying the HOG feature and application, detailing the process of HOG feature extracted and the design of classifiers in pedestrian detection. In this paper, pedestrian sample library was built and the Histogram of Oriented Gradient feature of the samples was extracted. The pedestrian classifier was obtained by using support vector machine and the performance of the classifier was tested.

Research for pedestrian detection classifier

The invention relates to a weighted sparse regular term constrained image denoising method based on group sparse representation, belonging to the technical field of image processing. The invention firstly divides the image to be processed into a plurality of image blocks with the same size and overlapping each other, K-nearest neighbor algorithm is used to cluster images into similar blocks, Secondly, the dictionary is obtained by principal component analysis algorithm for each similar image block group, The sparse coefficients of the similar image blocks are weighted and averaged to obtain the approximate estimation of the sparse coefficients of the original image group, and then the sparse coefficients of the current group are obtained by iterative shrinkage algorithm. Finally, the denoised images of the image block group are obtained according to the sparse coefficients of the group and the dictionary, and the denoised images are merged to obtain the denoised images. The invention can realize the effective solution of the sparse coefficient, achieves the good edge preservation and suppression artifact effect, and at the same time improves the running speed greatly.

A weighted sparse regular term constrained image denoising method based on group sparse representation is proposed

In target tracking, only the single feature description target and the traditional model updating method are difficult to adapt to the occlusion, deformation and various complex scene changes of the target. To solve this problem, a correlation filter tracking algorithm based on multi-feature fusion and selective update model is proposed. Firstly, the filter model is trained by direction gradient histogram and color feature respectively, and two features are fused according to the peak sidelobe ratio of the different characteristic response maps in the detection stage. The target location of the target is judged to be blocked by the peak sidelobe ratio of the final target position response map of each frame, and the model does not update when the target is blocked. The current model continues to be tracked in the next frame. A comparison experiment is made between 12 challenging video sequences and multiple forward moving target tracking algorithms. The results show that the average center position error is reduced by 25.12 pixels compared with the suboptimal Color Names, CN, and the average tracking accuracy is increased by 29.31%. The experimental results show that the algorithm can track the target steadily and accurately in the case of scale changes, occlusion, and illumination changes.

Robust Tracking Based on Multi-feature Fusion

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