When AWGN-based Denoiser Meets Real Noises (1904.03485v2)

Abstract: Discriminative learning-based image denoisers have achieved promising performance on synthetic noises such as Additive White Gaussian Noise (AWGN). The synthetic noises adopted in most previous work are pixel-independent, but real noises are mostly spatially/channel-correlated and spatially/channel-variant. This domain gap yields unsatisfied performance on images with real noises if the model is only trained with AWGN. In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN. First, we train a deep model that consists of a noise estimator and a denoiser with mixed AWGN and Random Value Impulse Noise (RVIN). We then investigate Pixel-shuffle Down-sampling (PD) strategy to adapt the trained model to real noises. Extensive experiments demonstrate the effectiveness and generalization of the proposed approach. Notably, our method achieves state-of-the-art performance on real sRGB images in the DND benchmark among models trained with synthetic noises. Codes are available at https://github.com/yzhouas/PD-Denoising-pytorch.

• The paper presents a dual-training strategy combining AWGN and RVIN with pixel-shuffle down-sampling to bridge the synthetic-real noise gap.
• It achieves state-of-the-art enhancements on the DND benchmark by raising PSNR and SSIM without relying on real noise data.
• The study offers actionable insights for improving denoising models in practical imaging systems with complex, spatially correlated noise.

Examining the Efficacy of AWGN-based Denoisers on Real Noises

The paper "When AWGN-based Denoiser Meets Real Noises" addresses a significant gap in the efficacy of image denoisers trained on synthetic Additive White Gaussian Noise (AWGN) when applied to real noisy images. Traditional denoising approaches, whether they rely on filtering techniques, low-rank approximations, sparse coding, or priors, have largely dealt with synthetic noise and shown limited adaptability to real-world noise which is not only pixel-independent but also spatially and channel-correlated.

Overview of the Proposed Method

The authors present a innovative method aimed at enhancing the performance of synthetic noise-trained denoisers on real noisy images. They introduce a model composed of a noise estimator and a denoiser, both of which are trained on a combination of AWGN and Random Value Impulse Noise (RVIN). This dual-noise training process seeks to bridge the domain gap by incorporating variations found in real noises. A novel adaptation technique, termed Pixel-shuffle Down-sampling (PD), is explored to further enhance the model's performance. This technique is founded on the hypothesis that spatially-correlated real noise can be reinterpreted as spatially-variant and pixel-independent noise through PD, thus making it more amenable to existing AWGN-based denoising models.

Experimental Analysis

The results from extensive experimentation underscore the effectiveness of the proposed method. It is demonstrated that the PD strategy, when integrated with the dual-trained denoiser, elevates the performance on real sRGB images within the DND benchmark to state-of-the-art levels. It's noteworthy that this marked enhancement is achieved without utilizing any real noise datasets during model training—illustrating the robustness and generalizability of the proposed approach. The quantitative improvements over existing techniques, as revealed through PSNR and SSIM metrics, validate the potential of this method to handle complex noise models, which are often spatially-variant and device-dependent, such as those witnessed in real imaging systems.

Implications and Speculations

The implications of this research are manifold. Practically, the adoption of the PD strategy could greatly enhance the applicability of denoising models in real-world scenarios without necessitating significant retraining or domain-specific adjustments—qualities highly valued in scalable software development and deployment. Theoretically, this approach challenges existing assumptions about the separability of noise handling from texture preservation, suggesting that a reevaluation of noise models might be beneficial for future enhancements. This work lays a foundation for further exploration into complex noise adaptation strategies and signals a step forward in the development of robust, versatile imaging solutions capable of handling diverse noise characteristics.

Future Directions

Looking forward, this paper sets the stage for several potential research pathways. There's an opportunity to explore alternative down-sampling strategies or hybrid approaches that might further improve the adaptable capabilities of denoisers. Additionally, integrating this model with other high-level vision tasks, such as segmentation or classification, could yield synergies that enhance overall system performance. Moreover, the strategy of pixel-shuffle adaptation could be examined in fields beyond image denoising, such as video processing, where temporal noise characteristics offer additional challenges.

In conclusion, this paper not only contributes a practical solution to a pressing issue within image processing but also enriches the theoretical discourse on noise modeling and adaptation, inviting further investigations that could redefine the landscape of image denoising technology.