On the Generalization of BasicVSR++ to Video Deblurring and Denoising (2204.05308v2)

Abstract: The exploitation of long-term information has been a long-standing problem in video restoration. The recent BasicVSR and BasicVSR++ have shown remarkable performance in video super-resolution through long-term propagation and effective alignment. Their success has led to a question of whether they can be transferred to different video restoration tasks. In this work, we extend BasicVSR++ to a generic framework for video restoration tasks. In tasks where inputs and outputs possess identical spatial size, the input resolution is reduced by strided convolutions to maintain efficiency. With only minimal changes from BasicVSR++, the proposed framework achieves compelling performance with great efficiency in various video restoration tasks including video deblurring and denoising. Notably, BasicVSR++ achieves comparable performance to Transformer-based approaches with up to 79% of parameter reduction and 44x speedup. The promising results demonstrate the importance of propagation and alignment in video restoration tasks beyond just video super-resolution. Code and models are available at https://github.com/ckkelvinchan/BasicVSR_PlusPlus.

• The paper demonstrates that minimal modifications to BasicVSR++ enable effective generalization from super-resolution to video deblurring and denoising, achieving up to 1.91 dB and 1.97 dB PSNR improvements respectively.
• It leverages second-order grid propagation and flow-guided deformable alignment to utilize long-term temporal information across video frames efficiently.
• The results highlight a favorable tradeoff between restoration accuracy and computational cost, outperforming transformer-based models in both efficiency and speed.

Generalization of BasicVSR++ for Video Restoration

The paper "On the Generalization of BasicVSR++ to Video Deblurring and Denoising" presents an exploration into the applicability of the BasicVSR++ framework across various video restoration tasks beyond its original video super-resolution objective. Through minimal architectural modifications, the research demonstrates how BasicVSR++, initially designed for video super-resolution, can effectively address video deblurring and denoising challenges, affirming the significance of long-term propagation and alignment in these contexts.

Framework Adaptation

BasicVSR++ builds on its predecessor BasicVSR by introducing enhancements such as second-order grid propagation and flow-guided deformable alignment. This progression leverages long-term temporal information more effectively across video frames. For video restoration tasks where the input and output resolutions are identical—such as deblurring and denoising—the paper suggests using strided convolutions to reduce input resolution, optimizing for efficiency without compromising performance. These adaptations allow the framework to operate with reduced computational overhead while maintaining competitive results.

Experimental Results

Quantitative and qualitative experiments underscore the efficacy of BasicVSR++ in video deblurring and denoising:

• Video Deblurring: The framework outperforms prior methods such as STFAN and TSP on datasets like DVD and GoPro, achieving PSNR improvements of up to 1.91 dB. Additionally, BasicVSR++ maintains superior efficiency with parameters and runtime favorably compared to state-of-the-art methods.
• Video Denoising: On datasets such as DAVIS and Set8, BasicVSR++ demonstrates remarkable gains in PSNR and SSIM, particularly at higher noise levels. For instance, a difference of 1.97 dB over the previous best in PSNR is observed on DAVIS, showcasing the framework's robustness against severe noise.

Comparison with Transformer-Based Approaches

Recent transformer-based solutions in video restoration show impressive results but often demand significantly higher computational resources. The research illustrates that BasicVSR++, with a reduced parameter count, achieves comparable or superior outcomes, especially when evaluating the performance-speed tradeoff. For instance, with input downsampling, BasicVSR++ achieves a notable balance between restoration accuracy and computational efficiency.

Implications and Future Directions

The findings highlight several key implications for the domain of video restoration:

1. Adapting Propagation Techniques: Efficient use of long-term information can be generalized beyond super-resolution to other restoration tasks, challenging the dominance of transformer-based models in terms of parameter efficiency.
2. Flexible Framework: The ability to adjust input resolution provides practical versatility, allowing researchers and practitioners to tailor the system for specific task demands, balancing speed and accuracy.
3. Continued Exploration: Future work could explore further customizations of the BasicVSR++ architecture to extend its applicability to additional domains or integrate more advanced alignment methods.

The research contributes to the theoretical understanding of video processing frameworks and opens pathways for more resource-efficient deployments in practical applications, such as real-time video enhancement scenarios.