Infrared image denoising based on the variance-stabilizing transform and the dual-domain filter

Cai

Security and Communication Networks

2021

In real life, images are inevitably interfered by various noises during acquisition and transmission, resulting in a significant reduction in image quality. The process of solving this kind of problem is called image denoising. Image denoising is a basic problem in the field of computer vision and image processing, which is essential for subsequent image processing and applications. It can ensure that people can obtain more effective information of images more accurately. This paper mainly studies a new method of crop image denoising with improved SVD in wavelet domain. The algorithm used in this study firstly carried out a 3-layer wavelet transform on the crop noise image, leaving the low-frequency subimage unchanged; then, for the high-frequency subimages distributed in the horizontal, vertical, and diagonal directions, the improved adaptive SVD algorithm was used to filter the noise; finally perform wavelet coefficient reconstruction. To effectively test the performance of the algorithm, field crop images were taken as test images, and the denoising performance of the algorithm, SVD algorithm, and the improved SVD algorithm used in this study were compared, and the peak signal-to--to-noise ratio (PSNR) was introduced. Quantitative evaluation of the denoising results of several types of algorithms. The experimental data in this paper show that when the noise standard deviation is greater than 20, the enhanced experimental results clearly achieve higher PSNR and SSIM values than WNNM. The average peak signal-to-noise ratio (PSNR) is about 0.1 dB higher, and the average SSIM is larger about 0.01. The results show that the algorithm used in this study is superior to the other two algorithms, which provides a more effective method for crop noise image processing.

Section: A Literature Reviewmentioning

confidence: 99%

A New Method of Denoising Crop Image Based on Improved SVD in Wavelet Domain

Cai

Security and Communication Networks

2021

“…42, the infrared image denoising model based on variance stabilized transform and dual domain filter proposed in Ref. 7, and the adaptive optimization filter based on multiclass CNN to remove impulse noise from digital images proposed in Ref. 43.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…Figure 5 shows the denoising results of different denoising models for IR1-IR8, where the first column shows the denoising results of the proposed model for infrared images, and the remaining columns show the denoising results of the models in Refs. 16, 7, and 41–43 for infrared images, respectively. As shown in Fig.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

See 1 more Smart Citation

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

2023

The traditional fractional order total variational model has better results in denoising and maintaining texture details in infrared images. However, it is difficult to determine the order of fractional order differentiation in image processing so that the model has the best denoising effect. To solve this problem, a fractional order total variational infrared image denoising model incorporating a flower pollination particle swarm optimization (PSO) algorithm is proposed in this paper. The model combines the search advantages of the flower pollination optimization algorithm and the PSO algorithm. The maximization multiobjective equation is designed as the fitness function of the optimization algorithm. The optimal order of the fractional order total variational model is found adaptively according to different features in different regions of the infrared image. The experimental results show that the improved model not only achieves the adaptivity of the adaption of the fractional order of total variational model order but also effectively removes the noise and retains the texture structure of infrared images to the maximum extent.

“…In addition to this, images can be enhanced by removing noise. For example, the image is denoised based on the variance-stabilizing transform and the dual-domain filter [ 25 ], which can suppress the mixed Poisson–Gaussian noise and preserve the details of the image. In the method based on wavelet coefficient threshold processing [ 26 ], the multiplicative noise is converted into additive noise according to the distribution characteristics of the noise, and the wavelet transform coefficients of the transformed infrared image are denoised.…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination

Jiang

Dong

Liang

2022

Sensors

Infrared image enhancement technology can effectively improve the image quality and enhance the saliency of the target and is a critical component in the marine target search and tracking system. However, the imaging quality of maritime infrared images is easily affected by weather and sea conditions and has low contrast defects and weak target contour information. At the same time, the target is disturbed by different intensities of sea clutter, so the characteristics of the target are also different, which cannot be processed by a single algorithm. Aiming at these problems, the relationship between the directional texture features of the target and the roughness of the sea surface is deeply analyzed. According to the texture roughness of the waves, the image scene is adaptively divided into calm sea surface and rough sea surface. At the same time, through the Gabor filter at a specific frequency and the gradient-based target feature extraction operator proposed in this paper, the clutter suppression and feature fusion strategies are set, and the target feature image of multi-scale fusion in two types of scenes are obtained, which is used as a guide image for guided filtering. The original image is decomposed into a target and a background layer to extract the target features and avoid image distortion. The blurred background around the target contour is extracted by Gaussian filtering based on the potential target region, and the edge blur caused by the heat conduction of the target is eliminated. Finally, an enhanced image is obtained by fusing the target and background layers with appropriate weights. The experimental results show that, compared with the current image enhancement method, the method proposed in this paper can improve the clarity and contrast of images, enhance the detectability of targets in distress, remove sea surface clutter while retaining the natural environment features in the background, and provide more information for target detection and continuous tracking in maritime search and rescue.