Google Pixel 4a Lens Shading: Difference between revisions

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In this project, we are going to utilize the photos taken using Google Pixel 4a to model the lens shading effect of Google Pixel 4a camera. We analysis the lens shading effects for different color channels, different exposure time and different isospeed. We compare our model with the standard costh4 model [2, 4, 5]. And finally, we build a pipeline of the lens shading correction using different images.
In this project, we are going to utilize the photos taken using Google Pixel 4a to model the lens shading effect of Google Pixel 4a camera. We analysis the lens shading effects for different color channels, different exposure time and different isospeed. We compare our model with the standard costh4 model [2, 4, 5]. And finally, we build a pipeline of the lens shading correction using different images.
[[File:Jiahui_fig1.png|600px|center|thumb|'''Figure 1''': Left: Geometric lens that caused the lens shading. Right: a picture with lens shading artifact (from [1]).]]
[[File:Jiahui_fig1.png|center|thumb|'''Figure 1''': Left: Geometric lens that caused the lens shading. Right: a picture with lens shading artifact (from [1]).]]


== Background ==
== Background ==

Revision as of 03:46, 27 November 2020

Jiahui Wang

Introduction

Lens shading, also known as vignetting, is common in optics and photography. As shown in Fig. 1 [1,2], the peripheral region of the image is darker than the center region. This phenomenon is intrinsic because of the geometric of the lens and thus the relative illumination at the edge is lower [2]. This interesting artifact has been widely used by photographer and artists to increase the depth and history of their photographs [1]. But we as an engineer should consider the correction of the artifact and provide people freedom to add the effect later on.

In this project, we are going to utilize the photos taken using Google Pixel 4a to model the lens shading effect of Google Pixel 4a camera. We analysis the lens shading effects for different color channels, different exposure time and different isospeed. We compare our model with the standard costh4 model [2, 4, 5]. And finally, we build a pipeline of the lens shading correction using different images.

Figure 1: Left: Geometric lens that caused the lens shading. Right: a picture with lens shading artifact (from [1]).

Background

Methods

Results

Conclusions

Slides and Codes

References