Joint space-time video super-resolution aims to increase both the spatial resolution and the frame rate of a video sequence. As a result, details become more apparent, leading to a better and more realistic viewing experience. This is particularly valuable for applications such as video streaming, video surveillance (object recognition and tracking), and digital entertainment. Over the last few years, several joint space-time video super-resolution methods have been proposed. While those built on deep learning have shown great potential, their performance still falls short. One major reason is that they heavily rely on two-dimensional (2D) convolutional networks, which restricts their capacity to effectively exploit spatio-temporal information. To address this limitation, we propose a novel generative adversarial network for joint space-time video super-resolution. The novelty of our network is twofold. First, we propose a three-dimensional (3D) attention mechanism instead of traditional two-dimensional attention mechanisms. Our generator uses 3D convolutions associated with the proposed 3D attention mechanism to process temporal and spatial information simultaneously and focus on the most important channel and spatial features. Second, we design two discriminator strategies to enhance the performance of the generator. The discriminative network uses a two-branch structure to handle the intra-frame texture details and inter-frame motion occlusions in parallel, making the generated results more accurate. Experimental results on the Vid4, Vimeo-90 K, and REDS datasets demonstrate the effectiveness of the proposed method.

Blurring and color distortion are significant issues in underwater optical imaging, caused by light absorption and scattering impacts in the water medium. This hinders our ability to accurately perceive underwater imagery. Initially, we merge two images and enhance both the brightness and contrast of the secondary images. We also adjust their weights to ensure minimal effects on the image fusion process, particularly on edges, colors, and contrast. To avoid sharp weighting transitions leading to ghost images of low-frequency components, we then propose and use a multi-scale fusion method when reconstructing the images. This method effectively reduces scattering and blurring impacts of water, fixes color distortion, and improves underwater image contrast. The experimental results demonstrate that the image fusion method proposed in this paper effectively improves the fidelity of underwater images in terms of sharpness and color, outperforming the latest underwater imaging methods by comparison in PSNR, Gradient, Entropy, Chroma, AG, UCIQE and UIQM. Moreover, this method positively impacts our visual perception and enhances the quality of the underwater imagery presented.

In this study, we propose a method to improve the image quality in turbid water by combining multiscale weighted features and attention mechanisms, effectively addressing issues such as reduced contrast and color casts. Initially, global color correction was employed using the grayscale space algorithm. Then, a preprocessed dataset was input into an improved Shallow-UWnet network for feature learning. The model included a multiscale weighted feature fusion module to extract global and local features and a parallel attention module to enhance key details and suppress noise in images. To simulate natural turbid water, a dataset of underwater images with different turbidities was constructed. Our method effectively corrected the color of images in different turbid water conditions and significantly enhanced image quality. In the evaluation on the custom dataset, performance improved by 13%–25% across standard image quality metrics compared with the second-best method. Additionally, we conducted generalization performance tests on the EUVP Dark, UIEBD, and UFO-120 datasets, validating the excellent enhancement effect of our method across different types of images. These results highlight the promising potential of our method for applications in underwater detection, rescue, and marine research.