Invertible Image Rescaling (2005.05650v1)

Abstract: High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images. However, typical image downscaling is a non-injective mapping due to the loss of high-frequency information, which leads to the ill-posed problem of the inverse upscaling procedure and poses great challenges for recovering details from the downscaled low-resolution images. Simply upscaling with image super-resolution methods results in unsatisfactory recovering performance. In this work, we propose to solve this problem by modeling the downscaling and upscaling processes from a new perspective, i.e. an invertible bijective transformation, which can largely mitigate the ill-posed nature of image upscaling. We develop an Invertible Rescaling Net (IRN) with deliberately designed framework and objectives to produce visually-pleasing low-resolution images and meanwhile capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process. In this way, upscaling is made tractable by inversely passing a randomly-drawn latent variable with the low-resolution image through the network. Experimental results demonstrate the significant improvement of our model over existing methods in terms of both quantitative and qualitative evaluations of image upscaling reconstruction from downscaled images.

• The paper introduces the Invertible Rescaling Net (IRN) that models downscaling and upscaling as an invertible bijective transformation to preserve high-frequency details.
• It employs a novel loss function combining high-resolution reconstruction, low-resolution guidance, and distribution matching to align the latent space with a Gaussian model.
• Experiments show a 4-5 dB PSNR improvement over traditional methods, demonstrating enhanced image fidelity and computational efficiency with a lower parameter count.

Invertible Image Rescaling: An Expert Overview

The paper "Invertible Image Rescaling" by Mingqing Xiao et al. introduces a novel approach to image downscaling and upscaling, proposing the Invertible Rescaling Net (IRN) to address the challenges posed by the ill-posed nature of these tasks. This research tackles the inherent data loss problem during image downscaling, which historically leads to suboptimal results when upscaling back to high resolutions. By conceptualizing the rescaling process as invertible transformations, the authors aim to preserve as much image fidelity as possible.

Core Contributions

1. Invertible Rescaling Net (IRN): The authors propose an innovative framework that models the downscaling and upscaling processes as an invertible bijective transformation. This approach uses a latent variable to capture the high-frequency information lost during downscaling, mitigating the ill-posedness typically observed in such transformations.
2. Novel Loss Function: A unique objective function is introduced, combining HR reconstruction loss, LR guidance loss, and distribution matching loss. This design ensures the separation of high and low-frequency details and encourages the alignment of the latent space distribution with a pre-defined distribution like Gaussian.
3. Efficiency and Performance: The architecture involves a carefully crafted use of wavelet transforms and dense blocks in the invertible neural network framework. The proposed IRN shows a significant improvement over existing methods in terms of both quantitative (e.g., PSNR and SSIM metrics) and qualitative assessments, while maintaining a lower parameter count, thus ensuring computational efficiency.

Results and Implications

The experimental results highlight a substantial enhancement over traditional super-resolution techniques—achieving a performance boost of 4-5 dB in PSNR across standard datasets. This suggests that IRN can be viable for practical applications where storage and bandwidth constraints necessitate downscaling, yet high fidelity reconstruction is desired upon transmission or display.

Furthermore, the analysis of different latent variable samples indicates that IRN effectively models the variability in high-frequency details without introducing perceptible artifacts. This ability to draw and utilize case-agnostic samples from a fixed distribution adds robustness to the model, enabling consistent reversion to high resolution across diverse image datasets.

Future Directions

The promising results from this paper open several avenues for future research:

• Enhanced Latent Space Modeling: Further exploration into alternative distributions or more complex latent space models may provide deeper insights into the structure of high-frequency content, potentially leading to even better reconstruction quality.
• Application to Video Sequences: Extending the invertibility concept to the temporal dimension could address similar challenges in video rescaling tasks, where temporal coherence and fidelity are of prime importance.
• Integration with Real-time Systems: Adaptation of IRN for real-time processing systems could leverage its computational efficiency, offering high-quality imaging solutions in domains such as cloud gaming or remote sensing.

Conclusion

This paper effectively addresses the long-standing issue of quality retention in image downscaling and upscaling through an innovative invertible framework. The IRN model sets a new benchmark for future research while providing practical insights and tools for improving image fidelity across varied applications. This contribution marks a significant step forward in the field of image processing, showcasing the potential of combining deep learning with invertible transformations to solve complex problems in computer vision.