Motivation Image Processing Flowchart Image Acquisition ... - Stacks [PDF]

High Resolution Starry Night Panoramas! Grant Yang! Department of Electrical Engineering, Stanford University

Motivation

Image Processing Flowchart

Generating panoramas of the night sky is nontrivial because of • Spatially varying motion of the stars due to the earth’s rotation • Low signal to noise ratio due to low light levels

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Final Image

This project integrates spatially variant image registration into the standard panorama workflow to allow automatic exposure stacking and panorama stitching of the night sky even with the inclusion of foreground elements Local Registration and Exposure Averaging

Spherical Projection

Image Acquisition! !

Register Frames

Contrast, Exposure, Sharpness Adjustments

Frame Blending

Experimental Results

Camera: Sony A77 Focal Length:16mm Aperture: f2.8 ISO: 3200 Shutter Speed: 15 sec

5 frames with camera rotated in 10 degree increments about the no parallax point 6 exposures/frame

Local Registration Algorithm! •  The motion of the stars is modeled

as an affine transformation •  The image is segmented into land and sky components via segmentation and morphological operations •  The stars are detected as high intensity peaks in the sky component •  SIFT descriptors and RANSAC are used to calculate the transformation between images •  The image is warped using a weighted sum of the transforms for each region

Noise Reduction:

Motion Correction:

A

B

C

Arrows indicate stars which have been recovered using exposure averaging (C) compared to original exposure (A), and noise reduction in Lightroom 5.1 (B)

A

B

C

Motion correction (A) results in a substantial improvement in sharpness over the uncorrected image (B). The spatially variant technique results in minimal noticeable artifacts at image boundaries (C)