The field of image restoration is rapidly advancing with the development of diffusion models, which have shown great promise in solving inverse problems such as image inpainting and super-resolution. Recent research has focused on improving the efficiency and accuracy of diffusion models, including the use of piecewise guidance schemes, latent diffusion-enhanced vector-quantized codebook priors, and adaptive path tracing. Notable papers in this area include Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance and UniLDiff: Unlocking the Power of Diffusion Priors for All-in-One Image Restoration.
In addition to image restoration, diffusion models are being explored for their potential in other applications, such as face retouching restoration and medical image restoration. The development of unified models that can handle diverse degradation conditions, such as adverse weather, underwater, and rain streaks, is also a growing area of research. Continual learning, spectral-based spatial grouping, and degradation-aware conditional diffusion models are some of the innovative approaches being used to improve the robustness and effectiveness of image restoration and enhancement techniques.
Beyond image restoration, researchers are also exploring the complex relationships between technology, environment, and society. The field of sustainable digital practices and policy decision-making is moving towards a more nuanced understanding of these relationships, with a growing recognition of the need to address systemic obstacles hindering digital de-escalation. Alternative design approaches, such as those proposed in Limits at a Distance and Computing, Complexity and Degrowth, are being developed to take into account the psychological distance between decision-makers and the environmental impacts of their policies.
The development of more sustainable materials and practices is also a key area of research, with a focus on reducing electronic waste, increasing energy efficiency, and promoting eco-friendly practices. Innovative approaches, such as solder-free circuit assembly methods and the use of liquid metal conductors, are being explored to enable easy reuse and recycling of electronic components. The assessment of the ecological impact of AI, including the carbon footprint and water usage of large language models, is also a growing area of research.
Overall, the emerging trends in image restoration and beyond are characterized by a growing focus on sustainability, diffusion models, and innovative approaches to addressing complex problems. As researchers continue to explore and develop new technologies and practices, it is likely that we will see significant advancements in the field of image restoration and beyond.
