Infrared stripe correction algorithm based on wavelet decomposition and total variation-guided filtering

Abstract

Stripe non-uniformity severely affects the quality of infrared images. It is challenging to remove stripe noise in lowtexture images without blurring the details. We propose a single-frame image stripe correction algorithm that removes infrared noise while preserving image details. Firstly, wavelet transform is used for multi-scale analysis of the image. At the same time, Total variation model is used for small window to smooth the original image. The small-scale total variation model can well preserve the edge information of the image, but it will leave stripe noise. Therefore, according to the prior knowledge of the vertical component of the stripe noise, the spatial filtering is finally performed: the smoothed image is used as the guide image for the stripe noise denoising. It is possible to prevent the lead filter from mistaking the strong stripe noise as edge detail, resulting in corrected image residual streak noise. The algorithm is systematically evaluated by experiments on simulated images and original infrared images, as well as compared with the current advanced infrared stripe non-uniformity correction algorithms. It is proved that our algorithm can better eliminate stripe noise and preserve edge details.

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“…As a multi-scale expression, wavelet analysis can provide both time-domain and frequency-domain information of the signal. It has many advantages in image and signal processing wavelet analysis, as a multi-scale expression, it can provide both timedomain and frequency-domain information of the signal [31,32]. Smooth processing and wavelet threshold is useful for signal denoising.…”

An Infrared Stripe Noise Removal Method Based on Multi-Scale Wavelet Transform and Multinomial Sparse Representation

“…To demonstrate the practicality and superiority of the algorithm proposed in this paper, we designed the control experiments in this section by taking into account the three state-of-the-art methods that currently exist. These three infrared image streak noise removal algorithms are multi-scale guided filtering method (MSGF) [ 7 ], gradient equalization method based on wavelet transform (WAGE) [ 11 ], and full variational method based on guided filtering (TVGF) [ 14 ]. All image data in the experiments were actually taken.…”

“…Later, some scholars built on this model and introduced a better denoising effect by using the L1 parametric description of the grayscale difference between the ideal image and the original image [ 13 ]. Some other scholars combined the three major types of methods, used frequency domain filtering to extract the streak noise component at high frequencies, and constructed a mathematical model with the gradient equilibrium of the ideal image to finally achieve the streak noise removal [ 14 ]. Currently, the most representative algorithm is the streak noise removal model based on group sparsity.…”

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“…Both traditional methods of attenuation calculation and Fourier transform have limitations. As a multi-scale expression, wavelet analysis provides both time and frequency domain information of signal at the same time, therefore it is widely used in image and signal processing [18,19]. Wavelet analysis is a method of time-frequency analysis of signals.…”