# A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation

arXiv: https://arxiv.org/abs/1704.08992 · HTML: https://mlsanity.com/paper/1704.08992/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2017.

## Built on by (most cited first, 25 of 25)

- [Explicit Visual Prompting for Low-Level Structure Segmentations](https://mlsanity.com/paper/2303.10883/) (2023)
- [Iterative Filter Adaptive Network for Single Image Defocus Deblurring](https://mlsanity.com/paper/2108.13610/) (2021)
- [Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions](https://mlsanity.com/paper/2108.09108/) (2021)
- [Learning to Deblur using Light Field Generated and Real Defocus Images](https://mlsanity.com/paper/2204.00367/) (2022)
- [Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image](https://mlsanity.com/paper/2110.05655/) (2021)
- [Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning](https://mlsanity.com/paper/2108.05251/) (2021)
- [Gaussian Kernel Mixture Network for Single Image Defocus Deblurring](https://mlsanity.com/paper/2111.00454/) (2021)
- [Explicit Visual Prompting for Universal Foreground Segmentations](https://mlsanity.com/paper/2305.18476/) (2023)
- [Neural Camera Simulators](https://mlsanity.com/paper/2104.05237/) (2021)
- [Image Deblurring by Exploring In-depth Properties of Transformer](https://mlsanity.com/paper/2303.15198/) (2023)
- [Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors](https://mlsanity.com/paper/2306.04506/) (2023)
- [Learning Single Image Defocus Deblurring with Misaligned Training Pairs](https://mlsanity.com/paper/2211.14502/) (2022)
- [Dynamic Neural Radiance Field From Defocused Monocular Video](https://mlsanity.com/paper/2407.05586/) (2024)
- [Learning Dual-Pixel Alignment for Defocus Deblurring](https://mlsanity.com/paper/2204.12105/) (2022)
- [Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair](https://mlsanity.com/paper/2304.04917/) (2023)
- [Attention! Stay Focus!](https://mlsanity.com/paper/2104.07925/) (2021)
- [Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged Annotations](https://mlsanity.com/paper/2410.16953/) (2024)
- [Efficient Concertormer for Image Deblurring and Beyond](https://mlsanity.com/paper/2404.06135/) (2024)
- [Reblurring-Guided Single Image Defocus Deblurring: A Learning Framework with Misaligned Training Pairs](https://mlsanity.com/paper/2409.17792/) (2024)
- [BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring](https://mlsanity.com/paper/2105.14766/) (2021)
- [Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild](https://mlsanity.com/paper/2211.14017/) (2022)
- [Single-image Defocus Deblurring by Integration of Defocus Map Prediction Tracing the Inverse Problem Computation](https://mlsanity.com/paper/2207.03047/) (2022)
- [SR-R2KAC: Improving Single Image Defocus Deblurring](https://mlsanity.com/paper/2307.16242/) (2023)
- [Dual-Camera All-in-Focus Neural Radiance Fields](https://mlsanity.com/paper/2504.16636/) (2025)
- [ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurring](https://mlsanity.com/paper/2606.06540/) (2026)

## Abstract (arXiv)

In this paper, we introduce robust and synergetic hand-crafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation. This paper systematically analyzes the effectiveness of different features, and shows how each feature can compensate for the weaknesses of other features when they are concatenated. For a full defocus map estimation, we extract image patches on strong edges sparsely, after which we use them for deep and hand-crafted feature extraction. In order to reduce the degree of patch-scale dependency, we also propose a multi-scale patch extraction strategy. A sparse defocus map is generated using a neural network classifier followed by a probability-joint bilateral filter. The final defocus map is obtained from the sparse defocus map with guidance from an edge-preserving filtered input image. Experimental results show that our algorithm is superior to state-of-the-art algorithms in terms of defocus estimation. Our work can be used for applications such as segmentation, blur magnification, all-in-focus image generation, and 3-D estimation.