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Seeing Mt. Rainier: Lucky Imaging for Multi-Image Denoising, Sharpening, and Haze Removal
Neel Joshi      Michael Cohen

Microsoft Research

Multi-Image Dehazing of Mount Rainier: Given multiple input images, a sequence of rigid and non-rigid alignment and per-pixel weighted averaging, minimizes blur, resampling, and alignment errors. Dehazing and contrast expansion then results in a sharp clean image.


Photographing distant objects is challenging for a number of reasons. Even on a clear day, atmospheric haze often represents the majority of light received by a camera. Unfortunately, dehazing alone cannot create a clean image. The combination of shot noise and quantization noise is exacerbated when the contrast is expanded after haze removal. Dust on the sensor that may be unnoticeable in the original images creates serious artifacts. Multiple images can be averaged to overcome the noise, but the combination of long lenses and small camera motion as well as time varying atmospheric refraction results in large global and local shifts of the images on the sensor.

An iconic example of a distant object is Mount Rainier, when viewed from Seattle, 90 kilometers away. This paper demonstrates a methodology to pull out a clean image of Mount Rainier from a series of images. Rigid and non-rigid alignment steps brings individual pixels into alignment. A novel local weighted averaging method based on ideas from “lucky imaging” minimizes blur, resampling and alignment errors, as well as effects of sensor dust, to maintain the sharpness of the original pixel grid. Finally dehazing and contrast expansion results in a sharp clean image.

Paper (4.91 MB)
Mt. Rainier Result
Cranes Result
Seattle Result
Full Dataset

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