Infrared images often carry obvious streak noises due to the non-uniformity of the infrared detector and the readout circuit. These streak noises greatly affect the image quality, adding difficulty to subsequent image processing. Compared with current elimination algorithms for infrared stripe noises, our approach fully utilizes the difference between the stripe noise components and the actual information components, takes the gradient sparsity along the stripe direction and the global sparsity of the stripe noises as regular terms, and treats the sparsity of the components across the stripe direction as a fidelity term. On this basis, an adaptive edge-preserving operator (AEPO) based on edge contrast was proposed to protect the image edge and, thus, prevent the loss of edge details. The final solution was obtained by the alternating direction method of multipliers (ADMM). To verify the effectiveness of our approach, many real experiments were carried out to compare it with state-of-the-art methods in two aspects: subjective judgment and objective indices. Experimental results demonstrate the superiority of our approach.

The technology of infrared dim- and small-target detection is irreplaceable in many fields, such as those of missile early warning systems and forest fire prevention, among others. However, numerous components interfere with infrared imaging, presenting challenges for achieving successful detection of infrared dim and small targets with a low rate of false alarms. Hence, we propose a new infrared dim- and small-target detection network, Multiscale Feature Extraction U-Net for Infrared Dim- and Small-Target Detection (MFEU-Net), which can accurately detect targets in complex backgrounds. It uses the U-Net structure, and the encoders and decoders consist of ReSidual U-block and Inception, allowing rich multiscale feature information to be extracted. Thus, the effectiveness of algorithms in detecting very small-sized targets can be improved. In addition, through the multidimensional channel and spatial attention mechanism, the model can be adjusted to focus more on the target area in the image, improving its extraction of target information and detection performance in different scenarios. The experimental results show that our proposed algorithm outperforms other advanced algorithms in detection performance. On the MFIRST, SIRST, and IRSTD-1k datasets, we achieved detection rates of 0.864, 0.962, and 0.965; IoU values of 0.514, 0.671, and 0.630; and false alarm rates of 3.08 × ${10}^{-5}$, 2.61 × ${10}^{-6}$, and 1.81 × ${10}^{-5}$, respectively.

The non-uniformity of infrared detectors’ readout circuits can lead to stripe noise in infrared images, which affects their effective information and poses challenges for subsequent applications. Traditional denoising algorithms have limited effectiveness in maintaining effective information. This paper proposes a multi-level image decomposition method based on an improved LatLRR (MIDILatLRR). By utilizing the global low-rank structural characteristics of stripe noise, the noise and smooth information are decomposed into low-rank part images, and texture information is adaptively decomposed into several salient part images, thereby better preserving texture edge information in the image. Sparse terms are constructed according to the smoothness of the effective information in the final low-rank part of the image and the sparsity of the stripe noise direction. The modeling of stripe noise is achieved using multi-sparse constraint representation (MSCR), and the Alternating Direction Method of Multipliers (ADMM) is used for calculation. Extensive experiments demonstrated the proposed algorithm’s effectiveness and compared it with state-of-the-art algorithms in subjective judgments and objective indicators. The experimental results fully demonstrate the proposed algorithm’s superiority and efficacy.

Enhancing low-light remote sensing images is crucial for preserving the accuracy and reliability of downstream analyses in a wide range of applications. Although numerous enhancement algorithms have been developed, many fail to effectively address the challenges posed by non-uniform illumination in low-light scenes. These images often exhibit significant brightness inconsistencies, leading to two primary problems: insufficient enhancement in darker regions and over-enhancement in brighter areas, frequently accompanied by color distortion and visual artifacts. These issues largely stem from the limitations of existing methods, which insufficiently account for non-uniform atmospheric attenuation and local brightness variations in reflectance estimation. To overcome these challenges, we propose a robust enhancement method based on non-uniform illumination compensation and the Atmospheric Scattering Model (ASM). Unlike conventional approaches, our method utilizes ASM to initialize reflectance estimation by adaptively adjusting atmospheric light and transmittance. A weighted graph is then employed to effectively handle local brightness variation. Additionally, a regularization term is introduced to suppress noise, refine reflectance estimation, and maintain balanced brightness enhancement. Extensive experiments on multiple benchmark remote sensing datasets demonstrate that our approach outperforms state-of-the-art methods, delivering superior enhancement performance and visual quality, even under complex non-uniform low-light conditions.

Low-light images have low contrast and high noise, making them not easily readable. Most existing image-enhancement methods focus on color images. In the present study, an enhancement and denoising algorithm for single low-light grayscale images is proposed. The algorithm is based on the multi-exposure fusion framework. First, on the basis of the low-light tone-mapping operators, the optimal virtual exposure image is constructed according to the information entropy criterion. Then, the latent low-rank representation is applied to two images to generate low-ranking parts and saliency parts to reduce noise after fusion. Next, the initial weight map is constructed based on the information contained in the decomposed images, and an adaptive weight refined algorithm is proposed to restore as much structural information as possible and keep the details while avoiding halo artifacts. When solving the weight maps, the decomposition and optimization of the nonlinear problem is converted into a total variation model, and an iterative method is used to reduce the computational complexity. Last, the normalized weight map is used for image fusion to obtain the enhanced image. The experimental results showed that the proposed method performed well both in the subjective and objective evaluation of state-of-the-art enhancement methods for low-light grayscale images.

Infrared target detection has a wide range of application value, but due to the characteristics of infrared images, infrared targets are easily submerged in the complex background. Therefore, in complex scenes, it is difficult to effectively and accurately detect infrared dim small targets. For this reason, we design an infrared dim small target (IDST) detection algorithm containing Large-size Receptive Fields (LRFNet). It uses the Residual network with an Inverted Pyramid Structure (RIPS), which consists of convolutional layers that become progressively smaller, so it can have a larger effective receptive field and can improve the robustness of the model. In addition, through the Attention Mechanisms with Large Receptive Fields and Inverse Bottlenecks (LRIB), it can make the network better localize the region where the target is located and improve the detection effect of the model. The experimental results show that our proposed algorithm outperforms other state-of-the-art algorithms in all evaluation metrics.