CFSNet: Toward a Controllable Feature Space for Image Restoration (1904.00634v2)

Abstract: Deep learning methods have witnessed the great progress in image restoration with specific metrics (e.g., PSNR, SSIM). However, the perceptual quality of the restored image is relatively subjective, and it is necessary for users to control the reconstruction result according to personal preferences or image characteristics, which cannot be done using existing deterministic networks. This motivates us to exquisitely design a unified interactive framework for general image restoration tasks. Under this framework, users can control continuous transition of different objectives, e.g., the perception-distortion trade-off of image super-resolution, the trade-off between noise reduction and detail preservation. We achieve this goal by controlling the latent features of the designed network. To be specific, our proposed framework, named Controllable Feature Space Network (CFSNet), is entangled by two branches based on different objectives. Our framework can adaptively learn the coupling coefficients of different layers and channels, which provides finer control of the restored image quality. Experiments on several typical image restoration tasks fully validate the effective benefits of the proposed method. Code is available at https://github.com/qibao77/CFSNet.

• The paper introduces CFSNet, a novel dual-branch framework that incorporates user control into image restoration processes.
• It employs adaptive coupling modules that interpolate between feature representations to balance perceptual quality and distortion metrics.
• Experimental results show improved PSNR and visual quality in tasks such as denoising and JPEG deblocking compared to state-of-the-art methods.

Overview of CFSNet: A Controllable Framework for Image Restoration

The paper, CFSNet: Toward a Controllable Feature Space for Image Restoration, introduces an interactive and user-controllable framework for enhancing image restoration processes. Traditional image restoration techniques often lack the flexibility and interactivity required to accommodate user preferences and adapt to varying degradation levels. This paper addresses these limitations by proposing the Controllable Feature Space Network (CFSNet), a novel deep learning framework designed to optimize image restoration tasks, such as super-resolution, denoising, and deblocking, with user-defined control over perceptual outcomes.

Framework Design

CFSNet seamlessly integrates control into the deep learning model, allowing users to adjust the restoration process through an input control variable, $\alpha_{in}$. Unlike deterministic networks that offer fixed solutions, CFSNet utilizes a dual-branch architecture composed of main and tuning branches, which learn features based on different optimization objectives. Each image restoration task is refined through a series of coupling modules that provide finer control over image quality by adaptive learning of coupling coefficients across the network's layers and channels.

The framework's adaptability is rooted in its ability to interpolate between feature representations, making use of the perceptual-distortion trade-off inherent in image restoration. This trade-off is controlled across tasks using an efficient coupling mechanism, allowing for smooth and continuous transition from distortion-focused to perception-focused results.

Performance Evaluation

The paper provides comprehensive validation of CFSNet across several image restoration tasks, demonstrating its effectiveness both quantitatively and qualitatively. Strong numerical results are highlighted in the experiments showing improved PSNR and visual quality when compared with state-of-the-art methods such as EDSR, DnCNN-B, and ESRGAN-I. For example, in image denoising tasks, CFSNet not only competes but often surpasses existing methods on unseen noise levels, demonstrating robust interpolation between practical degradation levels. Similarly, in JPEG image deblocking tasks, CFSNet outperforms competitors on unseen quality factors.

Theoretical Implications

CFSNet relies on the hypothesis that the data manifold can be flattened by neural network mapping, allowing interpolation between features in the latent space to mimic unknown points. The paper provides a detailed analysis of this approach, showing how adaptive coupling coefficients learned during training help achieve fine-grain control that is theoretically justified by manifold learning principles.

Practical Implications and Future Work

The adaptive control introduced by CFSNet offers practical benefits, providing users with the ability to tailor restoration outputs to their specific needs, whether it be noise reduction or detail preservation. This flexibility is vital for applications in mobile computing or scenarios where user preference variability is significant.

Future research could expand on applying CFSNet to other visual tasks and refining the control mechanism to cater to broader application needs, potentially including more sophisticated user input parameters or extending beyond visual tasks into other modalities in AI.

The framework establishes a foundation for interactive, perception-oriented image restoration, indicating promising new directions in controllable AI systems that can seamlessly adapt to human-centered design choices and real-world complexities. Overall, CFSNet's approach to balancing perceptual quality with traditional distortion metrics while providing user-specific control is a significant contribution to the field of image restoration and AI adaptability.