Examining the Impact of Blur on Recognition by Convolutional Networks

11/17/2016
by   Igor Vasiljevic, et al.
0

State-of-the-art algorithms for many semantic visual tasks are based on the use of convolutional neural networks. These networks are commonly trained, and evaluated, on large annotated datasets of artifact-free high-quality images. In this paper, we investigate the effect of one such artifact that is quite common in natural capture settings: optical blur. We show that standard network models, trained only on high-quality images, suffer a significant degradation in performance when applied to those degraded by blur due to defocus, or subject or camera motion. We investigate the extent to which this degradation is due to the mismatch between training and input image statistics. Specifically, we find that fine-tuning a pre-trained model with blurred images added to the training set allows it to regain much of the lost accuracy. We also show that there is a fair amount of generalization between different degrees and types of blur, which implies that a single network model can be used robustly for recognition when the nature of the blur in the input is unknown. We find that this robustness arises as a result of these models learning to generate blur invariant representations in their hidden layers. Our findings provide useful insights towards developing vision systems that can perform reliably on real world images affected by blur.

READ FULL TEXT

page 2

page 3

page 6

page 7

page 8

research
04/14/2016

Understanding How Image Quality Affects Deep Neural Networks

Image quality is an important practical challenge that is often overlook...
research
02/17/2022

Realistic Blur Synthesis for Learning Image Deblurring

Training learning-based deblurring methods demands a significant amount ...
research
05/05/2017

DeepCorrect: Correcting DNN models against Image Distortions

In recent years, the widespread use of deep neural networks (DNNs) has f...
research
06/10/2021

Data augmentation to improve robustness of image captioning solutions

In this paper, we study the impact of motion blur, a common quality flaw...
research
11/14/2018

Distortion Robust Image Classification with Deep Convolutional Neural Network based on Discrete Cosine Transform

State of the art CNN models for image classification are found to be hig...
research
11/27/2018

Learning to Synthesize Motion Blur

We present a technique for synthesizing a motion blurred image from a pa...
research
06/08/2020

Pixel-Wise Motion Deblurring of Thermal Videos

Uncooled microbolometers can enable robots to see in the absence of visi...

Please sign up or login with your details

Forgot password? Click here to reset