The field of image reconstruction and generation is moving towards more innovative and effective methods. Recent developments have focused on improving the fidelity and accuracy of image reconstruction, particularly in challenging scenarios such as dehazing and inversion attacks. Researchers are exploring new frameworks and techniques, including physics-grounded learning, momentum-based adaptive correction, and geometric-constrained approaches. These advancements have led to significant improvements in image quality and reconstruction accuracy, with potential applications in various fields. Notable papers include MAGIA, which introduces a novel label-inference-free framework for gradient inversion attacks, and HazeFlow, which proposes a physics-grounded learning approach for real-world dehazing. Additionally, Prompt-Guided Dual Latent Steering and Latent Iterative Refinement Flow have shown promising results in inversion problems and few-shot generation, respectively.