Explicit stripes in digital images have long posed challenges for computer vision tasks, which significantly disturbs visual perceptions and is not desirable for subsequent applications. Existing methods for destriping tasks, often constrained by image prior assumptions and lacking flexibility, demonstrate limited practical applicability. In response to these challenges, this paper proposes a flexible Destriping framework with arbitrary bounded Image Denoisers, called DID. The proposed framework decouples the destriping task into conditional expectation calculation and stripe estimation, and alternates between these two parts, which finally obtains the maximum likelihood estimation of the image. The former calculates the conditional expectation of the clean image given the estimated stripe, while the latter estimates the mean of each column in the residual image. To calculate the conditional expectation, this paper analyzes the equivalence between general image denoising and conditional expectation calculation based on Bayesian statistics. It is proven that the proposed DID framework flexibly incorporates existing denoisers to calculate the conditional expectation, without the need to explicitly define image prior assumptions. Furthermore, the fixed-point convergence of the DID framework is guaranteed postulating that the applied denoiser is bounded in an F-norm manner. Experimental results on both synthesized and real data validate the effectiveness and generalization of the proposed method, both quantitatively and qualitatively.

Due to the mechanism and structure of infrared (IR) imaging devices, IR sensors inevitably capture thermal radiation from non-imaging sources, resulting in thermal radiational bias fields superimposed on IR images. These bias fields manifest as low-frequency nonuniformities, which are difficult to disentangle from low-frequency image regions (background planes and object surfaces). Existing methods frequently fail to reliably isolate true bias fields in IR images containing extensive flat regions, resulting in reduced contrast and degraded intensity fidelity in the corrected outputs. To overcome this limitation, we investigate the geometric patterns underlying IR image degradation and make a critical observation: the unbiased images typically contain extensive flat regions, while in biased images the surface normals of those regions exhibit strong consistency with surface normals of the bias field. Leveraging this insight, we propose a novel bias field correction method that utilizes surface normal consistency as a structural prior. The proposed method begins with the local planar fitting to estimate surface normals from the observed image efficiently. Subsequently, the cosine similarity is computed between surface normals of adjacent blocks to identify regions dominated solely by bias field normals. Finally, a least squares optimization problem is formulated and solved to estimate the bias field. In addition, by exploiting the temporal consistency of the bias field, this method could be extended to IR video via a motion-frame accumulation framework. Extensive experiments on synthesized and real data demonstrate the generality and effectiveness of the proposed method.

Limited by the imaging mechanism, thermal radiation emitted from infrared (IR) imaging devices commonly contaminates the detector response to the scene, causing an additive thermal radiation bias at each pixel that dramatically reduces the contrast of the image. This degradation is considered a bias field over the image, impacting the visual perception and subsequent applications. Therefore, eliminating the thermal radiation bias field is an urgent issue. This paper proposes an adaptive thermal radiation bias field correction method using Huber function-based loss, which can adapt to image contents and hence preserve meaningful details while eliminating the bias field. The proposed method introduces the low-order bivariate polynomial surface model to fit the bias field from the observed image precisely. We establish a robust objective function based on the Huber function to estimate parameters of the surface model, which can adaptively switch between the ℓ1norm and the ℓ2norm-based loss functions according to the image region. Thus, our method not only effectively maintains optimality in flat regions but also improves robustness in edge & texture regions. To balance efficiency and robustness, we propose an adaptive threshold that controls the behavior of the Huber function. For stable convergence, an improved gradient descent strategy is utilized to solve the Huber loss-based objective function with the two-direction fitting and an adjustable step. Both simulated and real experiments against classical and state-of-the-art methods demonstrate the superior performance of the proposed method in improving the contrast and preserving details.

Due to the manufacturing imperfections, nonuniformities are ubiquitous in digital sensors, causing the notorious Fixed Pattern Noise (FPN). The ability of modern digital cameras to take images under low-light environments is severely limited by the FPN. This paper proposes a novel semi-calibration-based method for the FPN removal that utilizes a pre-calibrated Noise Pattern. The key observation of this work is that the FPN in each shot is actually a scaled Noise Pattern with an unknown scale parameter, since each pixel in the array generates a characteristic amount of dark current which is fundamentally determined by its physical properties. Given a noised image and the corresponding Noise Pattern, the scale parameter is automatically estimated, and then the FPN is removed by subtracting the scaled Noise Pattern from the noised image. The estimation of the scale parameter is based on an entropy minimization estimator, which is derived from the Maximum Likelihood principle and is further justified by subsequent analysis that minimizing the entropy uniquely identifies the true parameter. Convergence issues, as well as the optimality of the proposed estimator, are also theoretically discussed. Finally, some applications are given, illustrating the performance of the proposed FPN removal method in real-world tasks.

Digital images often suffer from the common problem of stripe noise due to the inconsistent bias of each column. The existence of the stripe poses much more difficulties on image denoising since it requires another ${n}$ parameters, where ${n}$ is the width of the image, to characterize the total interference of the observed image. This paper proposes a novel EM-based framework for simultaneous stripe estimation and image denoising. The great benefit of the proposed framework is that it splits the overall destriping and denoising problem into two independent sub-problems, i.e., calculating the conditional expectation of the true image given the observation and the estimated stripe from the last round of iteration, and estimating the column means of the residual image, such that a Maximum Likelihood Estimation (MLE) is guaranteed and it does not require any explicit parametric modeling of image priors. The calculation of the conditional expectation is the key, here we choose a modified Non-Local Means algorithm to calculate the conditional expectation because it has been proven to be a consistent estimator under some conditions. Besides, if we relax the consistency requirement, the conditional expectation could be interpreted as a general image denoiser. Therefore other state-of-the-art image denoising algorithms have the potentials to be incorporated into the proposed framework. Extensive experiments have demonstrated the superior performance of the proposed algorithm and provide some promising results that motivate future research on the EM-based destriping and denoising framework.

Hyperspectral imaging has great achievements in agriculture, astronomy, surveillance, and so on. However, the inherent low spatial resolution of hyperspectral imaging, unfortunately, limits its more widespread applications. Recently, hyperspectral image (HSI) super resolution addresses this problem by fusing a low spatial resolution HSI (LR‐HSI) with a high spatial resolution multispectral image (HR‐MSI), but most of these methods did not consider real‐time restoration of high spatial resolution HSI. In this study, the authors propose a fast HSI super‐resolution method which fills this blank. Specifically, they model the hyperspectral super resolution as a linear regression problem according to the fact that the imaging process is a linear transform and the inverse of this transform can be approximately estimated, as the spectra of a typical scene lie in a very low‐dimensional space. To further exploit the low‐dimensional nature of the spectra, they divide the HR‐MSI and LR‐HSI into several patches and learn the inverse transform patch‐by‐patch. Experiments on several public datasets show that their method approximates state‐of‐the‐art methods in accuracy, but is several orders of magnitude faster than all of them. Furthermore, they provide an efficient C language implementation of their methods, which can meet the real‐time request.