Stripe noise removal for infrared image by minimizing difference between columns

Stripe noise removal continues to be an active field of research for remote image processing. Most existing approaches are prone to generating artifacts in extreme areas and removing the stripe-like details. In this paper, a weighted double sparsity unidirectional variation (WDSUV) model is constructed to reduce this phenomenon. The WDSUV takes advantage of both the spatial domain and the gradient domain's sparse property of stripe noise, and processes the heavy stripe area, extreme area and regular noise corrupted areas using different strategies. The proposed model consists of two variation terms and two sparsity terms that can well exploit the intrinsic properties of stripe noise. Then, the alternating direction method of multipliers (ADMM) optimal solver is employed to solve the optimization model in an alternating minimization scheme. Compared with the state-of-the-art approaches, the experimental results on both the synthetic and real remote sensing data demonstrate that the proposed model has a better destriping performance in terms of the preservation of small details, stripe noise estimation and in the mean time for artifacts' reduction.

show abstract

“…In most model-based destriping methods, the output of a noisy image is formulated as an additive noise formulation [21,24,32], as follows:…”

Section: Stripe Variation Propertymentioning

confidence: 99%

Remote Sensing Images Stripe Noise Removal by Double Sparse Regulation and Region Separation

et al. 2018

View full text Add to dashboard Cite

show abstract

“…These noises are generally modeled as Gaussian noise and Poisson noise 4 – 7 Traditional infrared image denoising methods include spatial domain denoising methods and transform domain denoising methods 8 . The main spatial domain denoising methods are the mean value method and the wiener filtering method 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

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.

show abstract

“…Infrared imaging systems are an important tool used across many field domains, including medical imaging, transport navigation, and remote sensing [1]. Infrared images are typically corrupted by stripe noise due to the non-uniform sensing of light in the system's photo-receptive sensors [2]. This corruption results in significant fixed-pattern noise (FPN) embedded in the image, which decreases the quality of infrared imaging systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Unfolding for Iterative Stripe Noise Removal

Fayyaz

Daniel

Fayyaz

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

The non-uniform photoelectric response of infrared imaging systems results in fixed-pattern stripe noise being superimposed on infrared images, which severely reduces image quality. As the applications of degraded infrared images are limited, it is crucial to effectively preserve original details. Existing image destriping methods struggle to concurrently remove all stripe noise artifacts, preserve image details and structures, and balance real-time performance. In this paper we propose a novel algorithm for destriping degraded images, which takes advantage of neighbouring column signal correlation to remove independent column stripe noise. This is achieved through an iterative deep unfolding algorithm where the estimated noise of one network iteration is used as input to the next iteration. This progression substantially reduces the search space of possible function approximations, allowing for efficient training on larger datasets. The proposed method allows for a more precise estimation of stripe noise to preserve scene details more accurately. Extensive experimental results demonstrate that the proposed model outperforms existing destriping methods on artificially corrupted images on both quantitative and qualitative assessments.

show abstract

Stripe noise removal for infrared image by minimizing difference between columns

Cited by 25 publications

References 19 publications

Remote Sensing Images Stripe Noise Removal by Double Sparse Regulation and Region Separation

Remote Sensing Images Stripe Noise Removal by Double Sparse Regulation and Region Separation

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

Deep Unfolding for Iterative Stripe Noise Removal

Contact Info

Product

Resources

About