The Retinex-RAWMamba framework, primarily designed for low-light RAW image enhancement, can be extended to address other types of image degradation, such as blur and compression artifacts, by integrating additional processing modules tailored to these specific issues.
Incorporation of Blur Handling: To manage blur, a dedicated deblurring module could be integrated into the existing architecture. This module could utilize advanced techniques such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs) trained specifically on blurred images to learn the mapping from blurred to sharp images. By applying this module before the Retinex Decomposition Module (RDM), the framework can first restore sharpness, thereby enhancing the effectiveness of subsequent denoising and illumination correction processes.
Addressing Compression Artifacts: Compression artifacts, often introduced during image storage and transmission, can be mitigated by incorporating a compression artifact reduction module. This module could leverage techniques such as generative adversarial networks (GANs) or autoencoders that are trained on datasets containing compressed images. By learning to reconstruct high-quality images from their compressed counterparts, the framework can effectively reduce blockiness and ringing artifacts before applying the Retinex-based enhancements.
Multi-Task Learning: The framework could be adapted to a multi-task learning paradigm, where the model is trained simultaneously on various degradation types. This approach would allow the model to learn shared representations that are beneficial across different tasks, improving overall performance and robustness.
Adaptive Mechanisms: Implementing adaptive mechanisms that can detect the type of degradation present in an image would allow the framework to dynamically select the appropriate processing path. For instance, if significant blur is detected, the model could prioritize the deblurring module before proceeding to denoising and enhancement.
By extending the Retinex-RAWMamba framework in these ways, it can become a more versatile tool capable of handling a broader range of image degradation scenarios, thus enhancing its applicability in real-world imaging tasks.