Denoising Prior Driven Deep Neural Network for Image Restoration (1801.06756v2)

Abstract: Deep neural networks (DNNs) have shown very promising results for various image restoration (IR) tasks. However, the design of network architectures remains a major challenging for achieving further improvements. While most existing DNN-based methods solve the IR problems by directly mapping low quality images to desirable high-quality images, the observation models characterizing the image degradation processes have been largely ignored. In this paper, we first propose a denoising-based IR algorithm, whose iterative steps can be computed efficiently. Then, the iterative process is unfolded into a deep neural network, which is composed of multiple denoisers modules interleaved with back-projection (BP) modules that ensure the observation consistencies. A convolutional neural network (CNN) based denoiser that can exploit the multi-scale redundancies of natural images is proposed. As such, the proposed network not only exploits the powerful denoising ability of DNNs, but also leverages the prior of the observation model. Through end-to-end training, both the denoisers and the BP modules can be jointly optimized. Experimental results on several IR tasks, e.g., image denoisig, super-resolution and deblurring show that the proposed method can lead to very competitive and often state-of-the-art results on several IR tasks, including image denoising, deblurring and super-resolution.

• The paper introduces a novel DPDNN that unfolds iterative denoising steps into a trainable deep neural network, significantly enhancing image restoration quality.
• It employs a CNN-based denoiser and back-projection modules to leverage multi-scale redundancies, achieving superior PSNR compared to BM3D and MemNet.
• The framework effectively bridges model-based and learning-based techniques, promising practical improvements in surveillance, medical imaging, and super-resolution.

Overview of "Denoising Prior Driven Deep Neural Network for Image Restoration"

Constructed at the intersection of theory and practical application, the paper "Denoising Prior Driven Deep Neural Network for Image Restoration" presents an innovative approach towards addressing the complexities associated with image restoration (IR). Situating itself within a rich context of prior sparse coding and deep learning methodologies, this work introduces a novel denoising prior driven deep neural network (DPDNN), designed to facilitate the integration of denoising processes within a neural network framework effectively.

Problem and Methodology

The image restoration task, a critical aspect of computer vision, involves transforming a low-quality image to its high-quality counterpart. However, this inverse problem is fundamentally ill-posed, complicated further by diverse and unknown degradation parameters. Traditional model-based IR methods, anchored in Bayesian inference, and more recent learning-based approaches each present limitations regarding efficiency, flexibility, and adaptability.

In response, the authors propose a hybrid solution leveraging both paradigms' strengths. They advocate for a denoising-based IR algorithm whose iterative process is unfoldable into a deep neural network. Each layer of such a network mimics the procedural steps of the underlying denoising algorithm, interleaved with back-projection modules to handle data consistency. A central improvement here is the incorporation of a convolutional neural network (CNN)-based denoiser proficient in harnessing multi-scale redundancies inherent in natural images. Unlike its predecessors, this network can be optimally trained end-to-end to enhance both denoising efficacy and fidelity to the original observation model.

Core Contributions and Results

The paper's core contribution lies in the marriage of the denoising process with deep learning networks, allowing for an end-to-end optimized system that benefits from both well-tuned denoising priors and the raw computational power of DCNNs. By translating iterative denoising steps into neural network layers, the DPDNN framework not only advances theoretical understanding but achieves state-of-the-art results across several IR tasks including denoising, super-resolution, and deblurring.

Experimental results establish the paper's methodology as a strong contender in the image restoration domain. For instance, in tasks such as super-resolution and image denoising, the DPDNN framework significantly outperforms both classical regularization methods like BM3D and contemporary deep learning approaches like MemNet. These improvements are quantified by notable gains in Peak Signal-to-Noise Ratio (PSNR), indicating superior visual quality and detail retention in the restored images.

Implications and Future Work

The implications of this research are twofold. Practically, it offers an advanced framework capable of enhancing image quality across multiple domains, from surveillance and medical imaging to everyday consumer photography. Theoretically, it paves the way for future research to further integrate model-based insights with data-driven approaches, promoting more robust and adaptable IR methods.

This work motivates several trajectories for future developments. It encourages exploration into optimizing deeper network architectures that still respect the denoising principles outlined. Investigating the integration of alternative loss functions, such as perceptual or adversarial losses, could improve the perceptual quality of restored images. Additionally, extending the architecture for color image processing and experimenting with various noise models could broaden this framework's applicability.

Overall, this paper represents a vital step in evolving how deep learning can seamlessly incorporate domain-specific knowledge, enhancing both the performance and application scope of IR processes.