The field of image restoration and denoising is rapidly advancing with a focus on developing more robust and adaptive methods. Recent research has highlighted the importance of addressing error accumulation and noise approximation in diffusion models, with novel approaches such as reformulating inversion as an error-origin problem rather than an error-compensation problem. Additionally, there is a growing interest in exploiting flow-guided edge localization and circle grid targets for precise blur characterization, as well as developing more practical and accurate noise synthesis methods that require minimal data acquisition. Noteworthy papers include POLARIS, which improves inversion latent quality with negligible performance overhead, and CircleFlow, which achieves state-of-the-art accuracy and reliability in PSF estimation. Other notable papers include BlurDM, which seamlessly integrates the blur formation process into diffusion for image deblurring, and GuidNoise, which enables single-pair guided diffusion for generalized noise synthesis.