Fast End-to-End Trainable Guided Filter (1803.05619v2)

Abstract: Dense pixel-wise image prediction has been advanced by harnessing the capabilities of Fully Convolutional Networks (FCNs). One central issue of FCNs is the limited capacity to handle joint upsampling. To address the problem, we present a novel building block for FCNs, namely guided filtering layer, which is designed for efficiently generating a high-resolution output given the corresponding low-resolution one and a high-resolution guidance map. Such a layer contains learnable parameters, which can be integrated with FCNs and jointly optimized through end-to-end training. To further take advantage of end-to-end training, we plug in a trainable transformation function for generating the task-specific guidance map. Based on the proposed layer, we present a general framework for pixel-wise image prediction, named deep guided filtering network (DGF). The proposed network is evaluated on five image processing tasks. Experiments on MIT-Adobe FiveK Dataset demonstrate that DGF runs 10-100 times faster and achieves the state-of-the-art performance. We also show that DGF helps to improve the performance of multiple computer vision tasks.

• The paper introduces a novel, differentiable guided filtering layer that can be end-to-end trained within Fully Convolutional Networks (FCNs).
• It integrates this layer into a Deep Guided Filtering Network (DGF) that processes images coarse-to-fine, using the layer for efficient high-resolution upsampling.
• Experiments demonstrate the DGF network is significantly faster (10-100x) than alternatives while achieving state-of-the-art performance on various image processing tasks.

Fast End-to-End Trainable Guided Filter

The paper presents a novel building block for Fully Convolutional Networks (FCNs) known as the guided filtering layer, which fundamentally enhances FCN's efficiency in joint upsampling by providing an end-to-end trainable solution. This approach specifically addresses the limitations of FCNs in handling high-resolution image processing tasks due to significant computational and memory constraints.

Key Contributions

1. Differentiable Guided Filtering:
   • The traditional guided filter is reformulated into a fully differentiable block, allowing it to be jointly trained with FCNs.
   • The new guided filtering layer incorporates learnable parameters, increasing the adaptability to various tasks through data-driven optimization.
2. Integration with FCNs:
   • The guided filtering layer is integrated into a larger framework named the Deep Guided Filtering Network (DGF).
   • This framework operates in a coarse-to-fine manner, conducting operations at a lower resolution before using the guided filtering layer to enhance the output image resolution.
3. Performance and Efficiency:
   • The proposed DGF framework dramatically reduces computational time (running 10-100 times faster) while achieving state-of-the-art performance across multiple image processing benchmarks, such as image retouching and dehazing.
   • The network is scalable and applicable across various image processing and computer vision tasks.
4. Experimental Validation:
   • The experimental results on five image processing tasks demonstrate the superiority of the deep guided filtering network over other contemporary approaches in terms of both processing speed and output quality.
   • Additional experiments show DGF's applicability in improving the performance of computer vision tasks, including depth estimation and semantic segmentation.

Implications and Future Directions

The proposed guided filtering layer offers significant advantages in terms of efficiency and accuracy for pixel-wise image prediction tasks. Its integration as a trainable module within FCNs promises a reduction in the computational burden without sacrificing performance quality, suggesting potential use in real-time image processing applications where resources are limited.

Given its enhanced edge-preserving capability and flexibility, the guided filtering layer can be further explored and adapted to other domains that require high-resolution image outputs, such as medical imaging and autonomous driving systems. Future work may involve optimizing the guided filtering layer further for real-time implementations or extending this framework to three-dimensional data for volumetric image segmentation in medical fields. Additionally, exploring its effectiveness in video processing could also open new avenues in video enhancement technologies.

In summary, the novel approach presented in this paper of integrating a trainable guided filtering layer with FCNs suggest promising applications in a variety of fields, leveraging increased processing speed and accuracy to address complex image processing challenges efficiently.