With the growing adoption of high-resolution images in automated visual inspection, collaborative robotics, and intelligent surveillance, ensuring the authenticity and ownership of data during transmission and sharing across heterogeneous devices and cloud services has become a critical challenge in industrial informatics. Image watermarking plays a critical role in digital copyright protection, requiring a balance between imperceptibility, robustness, and embedding capacity. Although recent end-to-end deep learning methods achieve strong robustness, they exhibit poor scalability for multiresolution images, as fixed-resolution training demands retraining and incurs high computational costs. Watermarking methods based on pretrained deep networks using self-supervised learning (SSL) offer resolution flexibility. However, their embedding time grows exponentially with image resolution, limiting their applicability to high-resolution industrial scenarios. To overcome these limitations, we propose SSL-discrete wavelet transform (DWT), an efficient and robust watermarking framework that extends SSL approaches through the integration of the DWT. By restricting the iterative embedding process to the low-frequency subband of the DWT, SSL-DWT significantly reduces computational complexity while maintaining imperceptibility and robustness. Experimental results show that SSL-DWT improves computational efficiency by up to 19.2% in zero-bit watermarking and 18.7% in multibit watermarking compared with the SSL baseline, while achieving comparable or superior robustness against common distortions. These findings demonstrate that SSL-DWT provides a lightweight and scalable solution for copyright protection in high-resolution industrial image applications.

Light Field (LF) images provide rich visual representations of 3D scenes by capturing both spatial and angular information of light rays. However, their high dimensions present substantial challenges for conventional 2D image watermarking techniques in effectively ensuring copyright protection. In this work, we propose a deep learning-based Spatial-Angular Consistency waterMarking (SACMark) network, designed to address the unique challenges of watermark embedding and extraction in LF images. SACMark employs a spatial-angular feature extraction module to capture the multidimensional information of LF images and introduces consistency matching and fusion strategies to enhance feature utilization. The network adopts an encoder-noise-decoder architecture, optimized through adversarial training to improve the imperceptibility and robustness of the watermark. Experimental results demonstrate that SACMark maintains high visual quality across various embedding capacities and has minimal impact on depth estimation. Compared to traditional LF watermarking approaches and existing deep learning-based methods for 2D images, SACMark demonstrates improved resilience to noise while preserving essential LF characteristics. These findings suggest that SACMark holds promise for practical applications and may contribute to future developments in secure and adaptive LF image protection.

Abstract This paper proposes a new low‐light image enhancement method, which we call the Local Discrete Mapping Method. The new method limits the processing range to small areas with a high information relevance, which can better coordinate the enhancement quality of each area. First, the discrete mapping relationship of pixels (called discrete mapping points) globally occupying a small part of the critical gray value was extracted and designated to keep the enhancement amplitude of each local area consistent. Then, other free mapping points were adjusted according to the local features to achieve the best visual effect in each local area. In addition, this paper also proposes a span correction function that takes the gray span between local pixels as the adjustment object. The function can preserve the gray difference between freely mapped pixels to the maximum and significantly reduce detailed damage in the local area. Finally, we used 1500 test images and eleven objective evaluation indicators in the public dataset to comprehensively test the seven methods. The experimental results showed that the proposed method has an excellent dark area quality enhancement, brightness detail protection, overall noise suppression, and processing speed. It is significantly better than similar methods in terms of visual quality and quantitative testing.

The existing low-light image enhancement methods may cause under enhancement, unbalanced brightness and blurriness. To address these shortcomings, we proposed the non-linear mapping method based on the Retinex theory (NMMR). We use an improved traditional gamma function to estimate the reflectance, and we proposed the maximum brightness channel to estimate the illumination. The main steps of this approach can be described as follows: First, we convert the image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and the V channel is processed to estimate the reflectance and illumination. Then, we use a piecewise function to stretch the gray level dynamic range to achieve contrast enhancement. Finally, we use the fast pixelwise method to correct the color saturation and convert the image to the RGB color space. The experimental results show that the proposed method has lower computational complexity, the enhanced image has better objective and subjective evaluation results than other state-of-the-art methods.

Abstract Improving current efficiency and reducing energy consumption are two important technical goals of the electrolytic aluminum process (EAP). However, because the process involves complex noise characteristics (i.e., unknown types, redundant distributions and variable forms), it is very difficult to accurately develop a multiobjective prediction model. To overcome this problem, in this paper, a novel framework of multiobjective incremental learning based on a multi-source filter neural network (MSFNN) is presented. The proposed framework first presents a “multi-source filter” (MSF) technique that utilizes the mean and variance in the unscented Kalman filter (UKF) to guide the importance function of the particle filter (PF) based on a density kernel estimation method. Then, the MSF is embedded in the mutated neural network to adjust weights in real time. Third, weights are calculated and normalized by a modified importance function, which is the basis for further optimizing a secondary sampling based on sampling importance resampling (SIR). Finally, the incremental learning model with two objectives (i.e., process power consumption and current efficiency) based on the MSFNN in the EAP is established. The presented framework has been verified by the real-world EAP and some closely related methods. All test results indicate that the MSFNN’s relative prediction errors of the above two objectives are controlled within 0.51% and 0.38%, respectively and prove that MSFNN has significant competitive advantages over other recent filtering network models. Successfully establishment of the proposed framework provides a model foundation for multiobjective optimization problems in the EAP.

Abstract A novel night‐time image enhancement approach was proposed in this paper to address the problems of low contrast and poor details of low‐illumination images captured at night. To begin with, the luminance component V was extracted that was irrelevant to the colour information of the image upon converting the image to the HSV space from the RGB space. Then, by converting the luminance component V of the image into the probability space, the image was divided into a small‐probability grey‐scale area and a normal area based on the theory of probability. Moreover, pixels were transferred from the small probability area to the normal area of the image according to the nearest attribution principle that was established. Lastly, the contrast enhancement of the image was realized thanks to lossless mapping functions without losing the number of grey levels of the image. As can be observed from experimental results, the proposed method is superior to the most advanced algorithm in visual quality and quantitative measurement.