Effective three-step method for efficient correction of stripe noise and non-uniformity in infrared remote sensing images

Abstract

The non-uniformity inherent in infrared detectors’ readout circuits often manifests as stripe noise, significantly impacting the interpretability and utility of infrared images in remote sensing applications. This paper introduces a novel three-step approach designed to overcome the challenges posed by stripe noise, offering a balance between real-time performance, detail preservation, and noise suppression. The proposed method involves subtracting the average of image columns from the noisy image, adding the wavelet denoised average signal to the subtraction result, and finally correcting the resulting image using an image-guidance mechanism. This unique three-step process ensures effective noise removal while preserving image details. The incorporation of wavelet transform leverages the sparsity of noise in the wavelet domain, enhancing denoising without introducing blurring. In a further refinement, the third step utilizes an image-guidance mechanism to recover small details with increased precision. This comprehensive approach addresses both stripe noise and non-uniformity, offering an easy, efficient, and fast technique for image correction. A comprehensive set of experiments, which involves comparisons with state-of-the-art algorithms, serves to substantiate the effectiveness and superior performance of the proposed method in real-world remote sensing and infrared images. Various examples, encompassing both real and artificial noise, are presented to showcase the robustness and applicability of our approach.