$\mathbf{D^3}$: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images (1601.04149v3)

Abstract: In this paper, we design a Deep Dual-Domain ($\mathbf{D^3}$) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. We further design the One-Step Sparse Inference (1-SI) module, as an efficient and light-weighted feed-forward approximation of sparse coding. Extensive experiments verify the superiority of the proposed $D^3$ model over several state-of-the-art methods. Specifically, our best model is capable of outperforming the latest deep model for around 1 dB in PSNR, and is 30 times faster.

• The paper introduces D3, a Deep Dual-Domain model integrating deep learning with JPEG-specific knowledge from pixel and DCT domains for artifact removal.
• The D3 architecture uses a novel One-Step Sparse Inference module in the DCT domain and pixel-domain processing, enhanced by a box-constrained loss function.
• D3 significantly outperforms existing methods (e.g., AR-CNN) in PSNR (by ~1 dB) and speed (over 30x faster), enabling real-time high-definition JPEG restoration.

Review of $\mathbf{D^3}$: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images

The paper under review presents an innovative approach to artifact removal in JPEG-compressed images, leveraging a Deep Dual-Domain ($\mathbf{D^3}$) methodology. It uniquely integrates both deep learning capacities and problem-specific insights derived from prior knowledge of JPEG compression and sparsity-based dual-domain frameworks.

Technical Contributions and Methodology

The primary contribution of the paper is the design and implementation of the $\mathbf{D^3}$ model, which marries deep learning with problem-specific JPEG restoration principles. Traditional JPEG compression typically introduces artifacts such as blockiness and blurring due to its lossy compression nature, impacting perceptual quality and downstream image processing tasks. The authors strategically use dual-domain knowledge—comprising pixel and discrete cosine transform (DCT) domains—to improve restoration outcomes.

The authors structure the $\mathbf{D^3}$ model into two primary stages. In the DCT domain, the model employs a novel One-Step Sparse Inference (1-SI) module, which approximates sparse coding processes typically carried out through iterative optimization, but here is achieved through a feed-forward, neural network-compatible form. Following this, the model transitions to pixel-domain processing, further enhancing high-frequency feature recovery while simultaneously suppressing quantization noise-induced artifacts.

Distinctively, the model incorporates a box-constrained loss function to effectively utilize known quantization intervals during restoration, contributing significantly to its robustness and performance. This architectural design provides both interpretability and improved effectiveness over general deep models.

Performance Evaluation

Extensive experiments demonstrate the superiority of $\mathbf{D^3}$ over existing methodologies, including the Artifacts Reduction Convolutional Neural Networks (AR-CNN) and sparsity-based dual-domain approaches. Notably, the $\mathbf{D^3}$ model outperforms these methods in PSNR metrics by approximately 1 dB and exhibits a remarkable increase in processing speed—achieving over 30 times faster inference compared to AR-CNN, thus making it highly suitable for real-time processing in both high-definition and ultra-high-definition television contexts.

Implications and Future Directions

From a practical perspective, the $\mathbf{D^3}$ model offers a viable solution for real-time JPEG artifact removal, aligning efficiently with industry needs for fast and reliable image post-processing. Theoretically, the integration of domain-specific priors with deep learning represents a fruitful area for further exploration, potentially applicable to other compression types or forms of lossy data degradation.

Future research might focus on adapting the $\mathbf{D^3}$ framework to handle various compression algorithms beyond JPEG, as well as scaling the approach to tackle high-resolution images or video streams. The profound interplay between deep networks and traditional signal processing methodologies as demonstrated in this work could inspire further hybrid models that capitalize on computational advancements while maintaining a basis in domain expertise.