Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization

Abstract

In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.

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

“…There is no noise-like pattern in other features such as shape compared to objects. The stripe noise is basically present in the high-frequency components of the image [36]. This paper performs multi-scale representation of the image and decomposes the vertical component of the image.…”

“…where E and F represent the image blocks extracted from the corrected image and raw image by sliding window, and σ E , σ F , μ E , μ F , σ EF represent the standard deviation, mean value and cross-correlation of E and F. p 1 and p 2 take constants is to avoid errors where the denominator is zero in the arithmetic division..In the experimental measurement results, The maximum value of SSIM is 1.The closer to 1 the better the effect of maintaining the edge structure information of the corrected image [36]. Table 1 and Table 2 calculate the PSNR value and SSIM value of several methods, which are contains different stripe noise intensities.…”

“…Wavelet analysis technology can unfold the image hierarchically according to the wavelet base, and can determine which level to expand to according to the nature of the image and the image processing requirements given in advance, so that not only can the amount of calculation be effectively controlled, but also the image can be satisfied real-time processing requirements [34][35][36] . The wavelet transform separates the original image from different resolution details with different scales [37]..…”

Super-Resolution Reconstruction Algorithm for Infrared Image with Double Regular Items Based on Sub-Pixel Convolution

“…Then the maximization problem is transformed into the minimization problem. The formula is as Formula (17):…”

“…In the evaluation of simulation image test results, subjective evaluation and objective evaluation are used in this paper. The subjective evaluation is the result of human observation [17]. The objective evaluation indexes include peak signal-to-noise ratio (PSNR) and structural similarity degree (SSIM).…”