ARM: Any-Time Super-Resolution Method (2203.10812v2)

Abstract: This paper proposes an Any-time super-Resolution Method (ARM) to tackle the over-parameterized single image super-resolution (SISR) models. Our ARM is motivated by three observations: (1) The performance of different image patches varies with SISR networks of different sizes. (2) There is a tradeoff between computation overhead and performance of the reconstructed image. (3) Given an input image, its edge information can be an effective option to estimate its PSNR. Subsequently, we train an ARM supernet containing SISR subnets of different sizes to deal with image patches of various complexity. To that effect, we construct an Edge-to-PSNR lookup table that maps the edge score of an image patch to the PSNR performance for each subnet, together with a set of computation costs for the subnets. In the inference, the image patches are individually distributed to different subnets for a better computation-performance tradeoff. Moreover, each SISR subnet shares weights of the ARM supernet, thus no extra parameters are introduced. The setting of multiple subnets can well adapt the computational cost of SISR model to the dynamically available hardware resources, allowing the SISR task to be in service at any time. Extensive experiments on resolution datasets of different sizes with popular SISR networks as backbones verify the effectiveness and the versatility of our ARM. The source code is available at https://github.com/chenbong/ARM-Net.

• The paper introduces ARM, an adaptive single image super-resolution method that dynamically adjusts computation based on image patch complexity and available hardware resources.
• ARM uses a supernet of weight-sharing subnets with an Edge-to-PSNR lookup table to select the optimal subnet for image patches by complexity.
• Experiments demonstrate ARM improves computation-performance trade-off over prior methods, enabling efficient real-time super-resolution on various hardware.

Overview of "ARM: Any-Time Super-Resolution Method"

The paper "ARM: Any-Time Super-Resolution Method" introduces a novel approach to single image super-resolution (SISR), tackling key challenges related to computational efficiency and flexibility under varying hardware capabilities. Authored by Bohong Chen et al., the ARM method seeks to enhance the performance of SISR models by dynamically adjusting computation based on image complexity and available resources.

Core Contribution

ARM extends traditional SISR methods by introducing an adaptive framework that allows for real-time deployment on resource-constrained devices. It leverages dynamically adjustable subnets within a supernet, which share weights and vary in size, enabling computational redundancy to be minimized without compromising performance. The novelty lies in the utilization of an Edge-to-PSNR lookup table, facilitating efficient mapping of edge information to estimated PSNR, thus guiding the selection of subnets for processing image patches with different complexities.

Methodology

1. Observations and Motivation:
   • The efficacy of different-sized SISR networks varies with image patch complexity, warranting adaptive computational strategies.
   • There exists a tradeoff between computational overhead and image reconstruction quality, necessitating a balanced approach.
   • Edge information presents a viable metric for assessing PSNR, offering a computationally light alternative for estimating image patch complexity.
2. Supernet Architecture:
   • ARM employs a supernet comprising multiple SISR subnets with diverse sizes, which share weights and can dynamically adapt to hardware constraints.
   • This architecture allows the model to adjust computational loads without additional parameters, catering to varying resource availability and image patch complexity.
3. Inference Strategy:
   • An efficient edge detection mechanism is utilized to assign an edge score to each image patch.
   • Pre-built Edge-to-PSNR lookup tables enable rapid estimation of potential PSNR outcomes across subnets for any given patch.
   • A computation-performance tradeoff function selects the optimal subnet based on estimated PSNR and computational cost, ensuring effective super-resolution with minimal overhead.

Experimental Results

Extensive experimentation demonstrates ARM's effectiveness across several datasets, including DIV2K and other high-resolution benchmarks. The method consistently outperforms previous models like ClassSR in computation-performance tradeoff, achieving enhanced PSNR with reduced computational demands. The inference process is validated as robust and efficient across diverse hardware environments, emphasizing ARM's capability for real-time application.

Implications and Future Directions

ARM presents a significant leap in SISR by facilitating adaptive resource allocation and enhancing model flexibility. Its implications are far-reaching for applications requiring real-time, high-quality image reconstruction on mobile and embedded platforms. The concept of dynamic network adaptation showcased by ARM can extend beyond SISR, inspiring innovations in other domains requiring resource-aware solutions.

Future research could explore:

• Further refinement of edge detection techniques to enhance accuracy and efficiency.
• Expansion of ARM's adaptability to a wider range of image complexities and hardware configurations.
• Exploration of ARM-like architectures in other vision tasks, such as image denoising and compression.

Overall, ARM stands out as a compelling advancement in SISR, leveraging adaptive network configurations to balance computational overhead and image quality effectively. The method's innovative approach to real-time deployment ensures its relevance and utility in contemporary and future image processing applications.