The field of image and signal processing is moving towards more adaptive and dynamic approaches, leveraging advances in deep learning and neural networks to improve the precision and accuracy of various tasks. One of the key directions is the development of content-adaptive convolutional neural networks, which can respond to local feature context and produce spatially adaptive kernels. This is particularly useful for tasks such as pansharpening, where the goal is to integrate high-resolution panchromatic images with lower-resolution multispectral images. Another area of focus is the use of generative adversarial networks (GANs) for image restoration, including underwater image restoration, where the challenge is to capture the full range of visual degradations. Additionally, researchers are exploring the use of multimodal data fusion and attention mechanisms to improve the accuracy of sound speed field construction in underwater environments. Noteworthy papers include RAPNet, which introduces a receptive-field adaptive convolutional neural network for pansharpening, and xOp-GAN, which proposes a novel GAN model with multiple expert generator networks for real-color underwater image restoration. MDF-RAGAN is also notable for its use of a multimodal data-fusion generative adversarial network for real-time underwater sound speed field construction, and BPAM for its bilateral grid-based pixel-adaptive multi-layer perceptron framework for real-time image enhancement.