Image denoising is a critical task in numerous real-world applications. This paper presents an innovative method for image denoising in the wavelet domain, extending the SURELET approach to handle spatially varying noise levels. Traditional methods often assume a constant noise level across the entire image, which is unrealistic in practical scenarios. Our proposed method estimates the noise level locally within small neighborhoods in the wavelet domain, adapting well to images with spatially varying noise. This approach effectively reduces both uniform and spatially varying noise, as demonstrated through extensive experiments on six test images with five distinct noise patterns. The results, evaluated using peak signal-to-noise ratio (PSNR), show that our method outperforms existing denoising techniques, particularly in scenarios with spatially varying noise. This study not only advances the state-of-the-art in image denoising but also highlights the importance of adaptive noise estimation in real-world applications.

In this paper, we propose a novel hyperspectral unmixing through the perturbed linear mixture model to take into account the spectral variability offset of the linear mixture model. In our proposed approach, we reformulate the LMM by adding a term to account for the spectral variations of endmember spectra of the dictionary. We use a white Additive Gaussian distribution for the perturbations in the LMM and employ the maximum likelihood estimation. Our proposed Augmented Gaussian LMM (AGLMM) employs the multiplicative updating rules to accelerate the convergence and exploit the sparsity of the unknown parameters. We evaluate our proposed unmixing approach on different datasets. Our results show the superior performance of the proposed AGLMM method over the state-of-the-art methods.

In this paper, we propose a novel method for Multispectral document image binarization (MSdB) through the Non-negative Matrix Factorization (NMF) approach. We propose a three-step MSdB-NMF framework: i) NMF-based feature extraction algorithm by introducing a new optimization problem; ii) post-processing method iii); apply any existing gray/RGB binarization scheme. In the first step, we extract N features out of B spectral bands (N < B) and their corresponding coefficient matrix. We introduce a novel objective formulation that considers the robustness (related to the noise and various types of degradations) and sparseness (related to the ratio of text pixels versus the background). We employ the multiplicative updating rules to solve the proposed minimization problem and prove the convergence of the proposed feature extraction algorithm. In the next step, we select an appropriate feature vector, equivalently the corresponding coefficient vector. We propose to select it either visually or automatically via a post-processing method, which uses the benchmark binarization methods as baseline. In the last step, we apply some existing binarization methods such as Sauvola and Howe over the selected coefficient vector. Our proposed binarization framework is applicable for any kind of MS or hyperspectral (HS) document image without considering any prior knowledge such as the side information about the spectral bands of MS/HS document image. We evaluate our proposed binarization framework over two MS document image datasets. The experimental results confirm that our proposed framework outperforms several state-of-theart binarization schemes including the winner of the contest in MS-TEx-2015.

In this paper, we introduce a method of hyperspectral unmixing in the linear mixing model with the given library of the constituent materials. The proposed algorithm employs an arctan function to approximate the l0norm in the minimization problem. This approximation makes the objective function smooth, facilitates the convergence and results in reduced reconstruction errors. We evaluate the proposed method and compare it with other methods via simulation. This reveals that the proposed method outperforms the state-of-the-art methods and results in higher reconstruction signal-to-noise-ratio.