The Learnable Collaborative Attention (LCoA) mechanism proposed in the context of single image super-resolution can be extended to other low-level vision tasks by adapting the concept of learnable sparsity and weight sharing to tasks such as image denoising, image inpainting, and image deblurring.
For image denoising, the LCoA can be utilized to capture long-range feature correlations in noisy images, allowing for more accurate denoising results by focusing on relevant features while reducing computational complexity. In the case of image inpainting, the LCoA can help in filling missing regions by leveraging the self-similarity of the image and collaboratively optimizing the similarity matrix across different abstraction levels. Similarly, in image deblurring tasks, the LCoA can enhance the feature representation and extraction capabilities by incorporating attention mechanisms and weight sharing biases.
By applying the principles of LCoA to these low-level vision tasks, it is possible to improve the efficiency and effectiveness of deep learning models in tasks beyond single image super-resolution, ultimately leading to better performance and quality in various image processing applications.