DesnowNet: Context-Aware Deep Network for Snow Removal (1708.04512v1)

Abstract: Existing learning-based atmospheric particle-removal approaches such as those used for rainy and hazy images are designed with strong assumptions regarding spatial frequency, trajectory, and translucency. However, the removal of snow particles is more complicated because it possess the additional attributes of particle size and shape, and these attributes may vary within a single image. Currently, hand-crafted features are still the mainstream for snow removal, making significant generalization difficult to achieve. In response, we have designed a multistage network codenamed DesnowNet to in turn deal with the removal of translucent and opaque snow particles. We also differentiate snow into attributes of translucency and chromatic aberration for accurate estimation. Moreover, our approach individually estimates residual complements of the snow-free images to recover details obscured by opaque snow. Additionally, a multi-scale design is utilized throughout the entire network to model the diversity of snow. As demonstrated in experimental results, our approach outperforms state-of-the-art learning-based atmospheric phenomena removal methods and one semantic segmentation baseline on the proposed Snow100K dataset in both qualitative and quantitative comparisons. The results indicate our network would benefit applications involving computer vision and graphics.

• The paper introduces DesnowNet, a context-aware CNN that integrates transparency prediction with pyramid atrous convolution to effectively remove snow from images.
• It leverages a specialized pyramid loss function and the extensive Snow100k dataset to capture diverse snowflake shapes and enhance image restoration.
• Experimental results show improved visual quality and object detection, demonstrating the framework’s practical benefits for outdoor imaging applications.

Overview of DesnowNet: Context-Aware Snow Removal

The paper "DesnowNet: Context-Aware Snow Removal" introduces a convolutional neural network-based approach for effectively removing snowflakes from images, an aspect often overlooked in atmospheric phenomena removal tasks. Snow exhibits diverse transparency and irregular shapes, posing unique challenges different from haze and rain. This research proposes leveraging a deep learning framework to tackle these specific characteristics of snow.

Key Contributions and Methodology

The authors present DesnowNet, a novel convolutional neural network (CNN) encompassing pyramid atrous convolution layers, specifically designed to manage and remove snow from images. Key components of the approach include:

• Transparency Prediction and Restoration: The framework focuses on predicting the transparency of snowflakes, facilitating the restoration of the obscured landscape by separating the translucent snow mask from the underlying image.
• Pyramid Atrous Convolution: This layer captures contextual features of snowflakes effectively by accommodating their diverse shapes and sizes.
• Pyramid Loss Function: Designed to handle the erratic contours of snowflakes, the function enhances the network's capability to accurately recognize and reconstruct obscured image portions.
• Snow100k Dataset: The authors construct a considerable synthetic dataset, Snow100k, featuring 100,000 images with varying snowflake transparency and shapes to train the model. The dataset includes 1,000 real-world images for subjective evaluation.

Experimental Analysis and Results

The paper underscores the model's superior performance against state-of-the-art methods targeting atmospheric phenomena like haze, fog, and rain. Empirical evaluations reveal that DesnowNet excels in terms of visual quality and prediction accuracy. Notably, the model outperforms others in subjective assessments using both synthetic and real-world images. The approach's effectiveness is demonstrated by improved object detection in snow-removed images, showcasing an application-oriented benefit of this research.

Implications and Future Directions

The implications of this work extend to enhancing imaging applications under severe weather conditions. By focusing on snow, the proposed methods contribute to the broader field of atmospheric condition correction, offering enhanced clarity and usability of outdoor imaging systems.

The snow removal task's transformation into a well-defined CNN problem opens up several avenues for future exploration. Possible extensions might involve the incorporation of multi-weather condition adaptability or real-time snow removal from video streams. Exploring the limitations imposed by opaque snowflakes and refining the aberration estimation may further improve performance.

Overall, the paper presents an innovative and specialized approach to a previously underexplored problem in computer vision, setting the foundation for subsequent advancements in the domain of advanced image restoration techniques utilizing deep learning.