defocus-blur

codeslake/IFAN

[CVPR 2021] Official PyTorch Implementation for "Iterative Filter Adaptive Network for Single Image Defocus Deblurring"

Abdullah-Abuolaim/defocus-deblurring-dual-pixel

Reference github repository for the paper "Defocus Deblurring Using Dual-Pixel Data". We introduce a deep neural network (DNN) architecture that uses the dual-pixel (DP) sub-aperture views to reduce defocus blur.

codeslake/DMENet

[CVPR 2019] Official TensorFlow Implementation for "Deep Defocus Map Estimation using Domain Adaptation"

lingyanruan/DRBNet

[CVPR Oral 2022] PyTorch Implementation for "Learning to Deblur using Light Field Generated and Real Defocused Images"

vinthony/depth-distillation

[ECCV 2020] Defocus Blur Detection via Depth Distillation

xinario/defocus_segmentation

LBP-based segmentation of defocus blur

panda5mt/depth_estimate_blur

Defocusによる画像ボケを利用した深度推定

Abdullah-Abuolaim/recurrent-defocus-deblurring-synth-dual-pixel

Reference github repository for the paper "Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data". We propose a procedure to generate realistic DP data synthetically. Our synthesis approach mimics the optical image formation found on DP sensors and can be applied to virtual scenes rendered with standard computer software. Leveraging these realistic synthetic DP images, we introduce a new recurrent convolutional network (RCN) architecture that can improve defocus deblurring results and is suitable for use with single-frame and multi-frame data captured by DP sensors.

Abdullah-Abuolaim/multi-task-defocus-deblurring-dual-pixel-nimat

Reference github repository for the paper "Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning". We propose a single-image deblurring network that incorporates the two sub-aperture views into a multitask framework. Specifically, we show that jointly learning to predict the two DP views from a single blurry input image improves the network’s ability to learn to deblur the image. Our experiments show this multi-task strategy achieves +1dB PSNR improvement over state-of-the-art defocus deblurring methods. In addition, our multi-task framework allows accurate DP-view synthesis (e.g., ~ 39dB PSNR) from the single input image. These high-quality DP views can be used for other DP-based applications, such as reflection removal. As part of this effort, we have captured a new dataset of 7,059 high-quality images to support our training for the DP-view synthesis task.

PD-Mera/pyblur

Modified blurring routines from pyblur

tisbuet/Real-time-distortion-classification-in-Laparoscopic-videos

kawchar85/Awesome-Deblurring-Resources

A curated list of research papers and datasets related to image and video deblurring.