Learned Image Downscaling for Upscaling using Content Adaptive Resampler (1907.12904v2)

Abstract: Deep convolutional neural network based image super-resolution (SR) models have shown superior performance in recovering the underlying high resolution (HR) images from low resolution (LR) images obtained from the predefined downscaling methods. In this paper we propose a learned image downscaling method based on content adaptive resampler (CAR) with consideration on the upscaling process. The proposed resampler network generates content adaptive image resampling kernels that are applied to the original HR input to generate pixels on the downscaled image. Moreover, a differentiable upscaling (SR) module is employed to upscale the LR result into its underlying HR counterpart. By back-propagating the reconstruction error down to the original HR input across the entire framework to adjust model parameters, the proposed framework achieves a new state-of-the-art SR performance through upscaling guided image resamplers which adaptively preserve detailed information that is essential to the upscaling. Experimental results indicate that the quality of the generated LR image is comparable to that of the traditional interpolation based method, but the significant SR performance gain is achieved by deep SR models trained jointly with the CAR model. The code is publicly available on: URL https://github.com/sunwj/CAR.

• The paper introduces a content adaptive resampler that dynamically generates image-specific resampling kernels to preserve key details for super-resolution.
• It integrates a differentiable upscaling module using the EDSR network to significantly improve PSNR and SSIM compared to traditional fixed methods.
• Joint training of the resampler and SR network produces low-resolution images that are optimally tailored for high-quality reconstruction in various applications.

Content Adaptive Resampler for Learned Image Downscaling

The paper discusses a novel approach to image downscaling and subsequent upscaling, focusing on optimizing the downscaling process based on the potential for improved super-resolution (SR). This work is part of ongoing efforts to leverage deep learning models to enhance image processing tasks traditionally handled by linear methods like bilinear and bicubic interpolation.

Methodology

Central to the proposed framework is the content adaptive resampler (CAR), which is designed to generate image-specific resampling kernels dynamically. Unlike traditional methods that apply fixed filters globally, these resampling kernels are tailored to the specific content of the high-resolution (HR) input image, thereby preserving pertinent details that facilitate efficient super-resolution. The approach integrates a differentiable SR module that aids in upscaling the low-resolution (LR) images back to HR, allowing for back-propagation of reconstruction error throughout the model.

The hierarchical design of the model employs the EDSR (Enhanced Deep Super-Resolution Network) as the upscaling component. The EDSR is selected for its capability to achieve superior SR performance via a streamlined application of deep residual learning techniques.

Experimental Findings

Quantitative evaluations show that the downscaled images produced using the CAR methodology offer a substantial improvement in facilitating SR when compared to fixed downscaling methods. Specifically, the end-to-end CAR model significantly boosts PSNR and SSIM metrics over various benchmarks such as Set5, Set14, BSD100, and Urban100, outperforming state-of-the-art models like DPID and L0-regularized downscaling algorithms when paired with the EDSR upscaling framework.

Moreover, the research demonstrates that training the CAR model jointly with SR networks allows for better adaptation, yielding LR images that are exceptionally amenable to high-quality SR reconstructions. The model's adaptability to various SR network architectures suggests its potential as a versatile component in image processing pipelines.

Architectural Insights

The CAR model's adaptability is further enhanced through its architecture, which employs convolutional neural networks with residual blocks. These blocks encode the context of the HR image before estimating content adaptive resampling kernel weights and positional offsets, refining the non-uniform sampling process. This layout ensures that kernel generation is sensitive to local image features, improving the maintenance of edge information and other critical details necessary for optimal SR.

Implications and Future Directions

The paper opens possibilities for the CAR model to replace traditional downscaling methods in scenarios where downstream tasks like SR are of primary importance. This replacement is not only more resource-efficient but also spurs improvements in SR, providing an impactful contribution to fields relying heavily on image resolution enhancement.

Future exploration can focus on extending the CAR model’s capacity to integrate seamlessly with other differentiable image manipulation operations, optimizing for a variety of output quality criteria beyond visual fidelity, such as perceptual metrics.

Conclusion

This work contributes notably to the domain of deep learning in image processing by presenting a content-adaptive approach to downscaling that effectively bridges to high-quality upscaling tasks. The CAR model stands as a highly effective alternative to fixed linear methods, offering substantial improvements through dynamic adaptation to image content, underscoring its potential applicability across various digital imagery applications.