A Method For Adding Motion-Blur on Arbitrary Objects By using Auto-Segmentation and Color Compensation Techniques

TL;DR

This paper presents a unified framework for adding motion blur to arbitrary objects in images by capturing multiple frames without blur, compensating for noise with color correction, and enabling HDR effects for fast-moving objects.

Contribution

It introduces a novel method combining auto-segmentation, color compensation, and multi-exposure techniques to add controllable motion blur on a per-object basis.

Findings

Effective motion blur addition demonstrated through experiments.

Color compensation reduces noise caused by sensor gain.

Method enables HDR imaging for fast-moving objects.

Abstract

When dynamic objects are captured by a camera, motion blur inevitably occurs. Such a blur is sometimes considered as just a noise, however, it sometimes gives an important effect to add dynamism in the scene for photographs or videos. Unlike the similar effects, such as defocus blur, which is now easily controlled even by smartphones, motion blur is still uncontrollable and makes undesired effects on photographs. In this paper, an unified framework to add motion blur on per-object basis is proposed. In the method, multiple frames are captured without motion blur and they are accumulated to create motion blur on target objects. To capture images without motion blur, shutter speed must be short, however, it makes captured images dark, and thus, a sensor gain should be increased to compensate it. Since a sensor gain causes a severe noise on image, we propose a color compensation algorithm based on non-linear filtering technique for solution. Another contribution is that our technique can be used to make HDR images for fast moving objects by using multi-exposure images. In the experiments, effectiveness of the method is confirmed by ablation study using several data sets.