Fast and Accurate Single Image Super-Resolution via Information Distillation Network (1803.09454v1)

Abstract: Recently, deep convolutional neural networks (CNNs) have been demonstrated remarkable progress on single image super-resolution. However, as the depth and width of the networks increase, CNN-based super-resolution methods have been faced with the challenges of computational complexity and memory consumption in practice. In order to solve the above questions, we propose a deep but compact convolutional network to directly reconstruct the high resolution image from the original low resolution image. In general, the proposed model consists of three parts, which are feature extraction block, stacked information distillation blocks and reconstruction block respectively. By combining an enhancement unit with a compression unit into a distillation block, the local long and short-path features can be effectively extracted. Specifically, the proposed enhancement unit mixes together two different types of features and the compression unit distills more useful information for the sequential blocks. In addition, the proposed network has the advantage of fast execution due to the comparatively few numbers of filters per layer and the use of group convolution. Experimental results demonstrate that the proposed method is superior to the state-of-the-art methods, especially in terms of time performance.

• The paper introduces a compact Information Distillation Network (IDN) that efficiently distills features for precise and fast single image super-resolution.
• It leverages stacked distillation blocks combining feature enhancement and compression to reduce computational cost while maintaining high reconstruction quality.
• The method demonstrates significant performance gains in PSNR and SSIM over state-of-the-art models, enabling real-time processing in resource-limited scenarios.

Fast and Accurate Single Image Super-Resolution via Information Distillation Network

The paper, "Fast and Accurate Single Image Super-Resolution via Information Distillation Network," introduces an innovative approach to tackle the inherent challenges in single image super-resolution (SISR) using deep convolutional neural networks (CNNs). The authors address the computational complexity and memory consumption associated with deep CNN architectures in SISR tasks by proposing a more efficient and compact model, termed the Information Distillation Network (IDN).

Key Contributions

The proposed IDN is characterized by its deep but compact architecture, which is achieved by integrating enhancement and compression units into what the authors describe as distillation blocks (DBlocks). The network is structured into three key components:

1. Feature Extraction Block (FBlock): This block extracts features directly from low-resolution images, leveraging a stack of convolutional layers.
2. Information Distillation Blocks (DBlocks): Stacked DBlocks progressively distill useful information. The enhancement unit within a DBlock aggregates different types of features, while the compression unit reduces redundancy, enabling efficient data processing.
3. Reconstruction Block (RBlock): Substantially aggregates the outputs from the DBlocks to synthesize the high-resolution image.

By employing group convolution and maintaining fewer filters per layer, the IDN achieves significant speed improvements over traditional methods, while also maintaining or exceeding reconstruction quality benchmarks.

Experimental Results

The authors provide a comprehensive evaluation of their method across standard datasets such as Set5, Set14, BSD100, and Urban100. The IDN demonstrates superior performance metrics, notably achieving high PSNR and SSIM values, positioning itself ahead of several other state-of-the-art methods like VDSR, DRCN, DRRN, and MemNet in terms of both speed and accuracy.

The paper particularly highlights substantial reductions in inference time when compared to methods of similar performance, thereby emphasizing the practicality of IDN for real-time applications. It is noteworthy that the method achieves real-time processing capabilities, making it suitable for deployment in mobile and embedded systems, where computational resources are limited.

Theoretical Implications

The results suggest that a balance between depth and efficiency can be achieved without compromising super-resolution quality. The architecture challenges the prevalent trend of deepening networks for performance improvement and offers an alternative via information distillation. This indicates potential for broader applications in neural network design across various domains of computer vision.

Future Directions

The authors suggest extending the principles of the proposed architecture to other image restoration tasks, such as denoising and compression artifact reduction. The generalization capabilities of such a network could open avenues for research into efficient and compact CNN architectures tailored to specific low-level vision problems.

In conclusion, the Information Distillation Network represents a significant contribution to the field of image super-resolution, offering an effective trade-off between performance and resource demands, with implications that potentially extend far beyond the immediate problem space explored in this paper.