Pyramid Real Image Denoising Network (1908.00273v2)

Abstract: While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noise and denoising, they still perform poorly on real-world noisy images. The main reason is that the real-world noise is more sophisticated and diverse. To tackle the issue of blind denoising, in this paper, we propose a novel pyramid real image denoising network (PRIDNet), which contains three stages. First, the noise estimation stage uses channel attention mechanism to recalibrate the channel importance of input noise. Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features. Third, the stage of feature fusion adopts a kernel selecting operation to adaptively fuse multi-scale features. Experiments on two datasets of real noisy photographs demonstrate that our approach can achieve competitive performance in comparison with state-of-the-art denoisers in terms of both quantitative measure and visual perception quality. Code is available at https://github.com/491506870/PRIDNet.

Pyramid Real Image Denoising Network

The paper "Pyramid Real Image Denoising Network" introduces an innovative approach for the denoising of real-world images, building upon the foundations laid by deep Convolutional Neural Networks (CNNs). Despite significant advancements in denoising methods for specific noise types such as additive white Gaussian noise (AWGN), these techniques falter when confronted with the complexity and diversity of real-world noise. This paper presents a novel architecture—PRIDNet—that aims to overcome these challenges with a three-stage network design.

Overview of PRIDNet

PRIDNet comprises three core stages: noise estimation, multi-scale denoising, and feature fusion. Each stage addresses specific limitations within traditional CNN approaches:

1. Noise Estimation Stage: This stage employs a channel attention mechanism to recalibrate the channel importance of input noise features. By refining the weighting of each channel, the network better distinguishes between more and less significant noise elements.
2. Multi-Scale Denoising Stage: Utilizing pyramid pooling, this stage extracts features at multiple scales, thereby ensuring a denoising approach that captures both global context and local details. This method is inspired by the global search strategy in traditional methods like BM3D, which has proved effective in accessing information beyond narrow receptive fields.
3. Feature Fusion Stage: This stage is characterized by a kernel selecting operation. By employing multi-branch convolutions with different kernel sizes, the model adaptively combines multi-scale features, facilitating enhanced spatial and channel specificity.

Experimental Validation

Experiments conducted on two real-world noisy datasets demonstrate notable efficacy of PRIDNet compared to several state-of-the-art denoising networks, inclusive of both blind and non-blind approaches. Results indicate significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), corroborating the efficacy of the proposed methodology.

Quantitatively, PRIDNet achieves a PSNR of 48.48 and an SSIM of 0.9806 in the raw domain, outperforming competing methods. In the sRGB domain, PRIDNet maintains competitive performance with a PSNR of 39.42 and an SSIM of 0.9528. Moreover, the model exhibits efficient performance in terms of processing time, taking approximately 0.05 seconds to process a 512x512 image, making it favorable for real-time applications.

Implications and Future Directions

The implications of this research extend beyond immediate improvements in image denoising. By leveraging advanced techniques such as channel attention and pyramid pooling within a unified architecture, PRIDNet exemplifies the potential for more adaptable and robust CNN models that can proficiently handle the intricacies of real-world noise. The kernel selecting operation further highlights the growing importance of adaptability and feature specificity in convolutional operations.

Future exploration may focus on broadening the applicability of PRIDNet across diverse image domains, including hyperspectral or medical imaging, where noise characteristics differ significantly. Additionally, investigations can dive deeper into the implications of pyramid structures for feature extraction and aggregation in various machine learning contexts, potentially leading to novel multi-scale approaches across different tasks in AI.

In conclusion, the Pyramid Real Image Denoising Network marks a meaningful contribution to denoising research, addressing prevalent challenges in handling non-Gaussian, real-world noise through sophisticated multi-stage processing.