# Defocus Blur Detection via Depth Distillation

arXiv: https://arxiv.org/abs/2007.08113 · HTML: https://mlsanity.com/paper/2007.08113/

## Key facts

- Cited by 12 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Explicit Visual Prompting for Low-Level Structure Segmentations](https://mlsanity.com/paper/2303.10883/) (2023)
- [Pre-training-free Image Manipulation Localization through Non-Mutually Exclusive Contrastive Learning](https://mlsanity.com/paper/2309.14900/) (2023)
- [Towards Diverse Binary Segmentation via A Simple yet General Gated Network](https://mlsanity.com/paper/2303.10396/) (2023)
- [Explicit Visual Prompting for Universal Foreground Segmentations](https://mlsanity.com/paper/2305.18476/) (2023)
- [Learning Single Image Defocus Deblurring with Misaligned Training Pairs](https://mlsanity.com/paper/2211.14502/) (2022)
- [DifferSeg: Towards Diverse Multimodal Binary Segmentation via Differential Perception and Frequency Guidance](https://mlsanity.com/paper/2606.08906/) (2026)
- [Camera-Independent Single Image Depth Estimation from Defocus Blur](https://mlsanity.com/paper/2311.13045/) (2023)
- [Depth and DOF Cues Make A Better Defocus Blur Detector](https://mlsanity.com/paper/2306.11334/) (2023)
- [Efficient Hybrid Zoom using Camera Fusion on Mobile Phones](https://mlsanity.com/paper/2401.01461/) (2024)
- [Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged Annotations](https://mlsanity.com/paper/2410.16953/) (2024)
- [Patch-wise Features for Blur Image Classification](https://mlsanity.com/paper/2304.03156/) (2023)
- [BOCCHI: A More Realistic and Challenging Benchmark for Local Motion Blur Detection with MSDCT-UNet](https://mlsanity.com/paper/2607.10427/) (2026)

## Abstract (arXiv)

Defocus Blur Detection(DBD) aims to separate in-focus and out-of-focus regions from a single image pixel-wisely. This task has been paid much attention since bokeh effects are widely used in digital cameras and smartphone photography. However, identifying obscure homogeneous regions and borderline transitions in partially defocus images is still challenging. To solve these problems, we introduce depth information into DBD for the first time. When the camera parameters are fixed, we argue that the accuracy of DBD is highly related to scene depth. Hence, we consider the depth information as the approximate soft label of DBD and propose a joint learning framework inspired by knowledge distillation. In detail, we learn the defocus blur from ground truth and the depth distilled from a well-trained depth estimation network at the same time. Thus, the sharp region will provide a strong prior for depth estimation while the blur detection also gains benefits from the distilled depth. Besides, we propose a novel decoder in the fully convolutional network(FCN) as our network structure. In each level of the decoder, we design the Selective Reception Field Block(SRFB) for merging multi-scale features efficiently and reuse the side outputs as Supervision-guided Attention Block(SAB). Unlike previous methods, the proposed decoder builds reception field pyramids and emphasizes salient regions simply and efficiently. Experiments show that our approach outperforms 11 other state-of-the-art methods on two popular datasets. Our method also runs at over 30 fps on a single GPU, which is 2x faster than previous works. The code is available at: https://github.com/vinthony/depth-distillation