While the content-consistent noising-and-denoising approach of FreeEnhance shows promising results, several limitations may arise. One potential limitation is the reliance on the quality of the initial input image. If the input image contains significant noise or artifacts, the enhancement process may inadvertently amplify these issues rather than resolve them. Future work could address this by integrating a preliminary denoising step that cleans the input image before applying the FreeEnhance framework.
Another limitation is the computational cost associated with the two-stage process, particularly in real-time applications. The current implementation may not be suitable for scenarios requiring rapid processing, such as live video enhancement. To mitigate this, future research could explore optimizing the algorithm for faster inference times, possibly through model distillation or pruning techniques that reduce the complexity of the diffusion model without sacrificing quality.
Additionally, the framework's performance may vary across different types of images, particularly those with complex textures or patterns. Future work could involve training the model on a more diverse dataset that includes a wider variety of image types, ensuring that the noising and denoising processes are robust across different content domains.