Graphics, Imaging, and Games Lab

Previous Work

Dr. Mould has previously examined procedures for controlling filtering mask expansion when the mask size is not constant throughout the image. The results are contained in his article on cumulative range geodesic filtering, available at http://gigl.scs.carleton.ca/node/481

In summary, Dr. Mould found that the following method applied the appropriate amount of smoothing depending on the degree to which a pixel was an outlier or part of an outlier region in the image:

Continuing the discussion of outlier removal from my previous post (http://gigl.scs.carleton.ca/node/543), I am demonstrating a case where the poor noise removal characteristics of the leaky integration filter is an advantage.

Introduction

In this post, leaky integration refers to a calculation similar to integration, except that the integrand is multiplied by an increasing exponential function. The exponential function has a value of 1 at the upper boundary of integration.

To produce the results shown in this post, I created a version of the cumulative range geodesic filter which uses leaky integration as follows: The distance to each pixel in a filtering mask is equal to the incremental distance between the current pixel and the previous pixel along the path from the source pixel, added to the leaky sum of the incremental distances between the other pixels along the path.

Introduction

In my last few posts, I explored global image recolouration, where I attempted to find some measurement of how "interesting" a pixel's original colour is and assign "uninteresting" pixels a new colour. I intended to insert features of a new image (a solid colour, in most cases), in a manner which did not eliminate the essential features of the original image.

In this post, I am presenting results from local image recolouration. Local recolouration involves blending the source image with a new image only in a specific region.

Introduction

Recolouring an image by averaging the image with a solid colour, as shown in my last two posts (http://gigl.scs.carleton.ca/node/538 and http://gigl.scs.carleton.ca/node/539), is a simple way to add contrast and visual interest to an image, provided that the average is not uniformly weighted across the image. I experimented with recolouration as kind of an end in itself, but also with the intention of predicting the results of a more general process, image retexturing.

The Residual

The "residual" from filtering, as used in this post, is the subtraction of the filtered image from the original image. Larger magnitudes in the residual indicate areas of higher-frequency colour variation or outliers in the source image.

Following-up on the idea mentioned in a previous post (http://gigl.scs.carleton.ca/node/536), I am experimenting with mask inclusion counts as a method for recolouring images.

In my last post (http://gigl.scs.carleton.ca/node/536), I mentioned I would analyze the plane-fitting filters from a past post (http://gigl.scs.carleton.ca/node/522) with respect to the technique I recently developed for analyzing noise pixel smoothing (see http://gigl.scs.carleton.ca/node/535). The results are provided below:

I decided to start exploring how to filter out noise pixels from an image by implementing an idea that Dr. Mould had brought up with earlier. Dr. Mould suggested that outliers could potentially be identified by the number of times that they are added to filtering masks for the surrounding pixels, relative to other pixels in the image. In order to use this measurement, I created a two-pass filter, in which the entire image is first processed at a given mask size and the number of times that each pixel is included in a filtering mask is recorded. Next, the image is filtered in a way which makes use of the data obtained during the first pass, using the same or a different mask size.

Motivation

In the near future, I plan to develop a number of variations on the cumulative range geodesic filter, with the intention of building a filter having the following properties:

• Noise pixels in the image (outliers) are strongly smoothed towards neighbouring colours, and neighbouring colours are not contaminated by noise colours. To be specific, noise pixels are pixels whose colours are very different from the colours of their surroundings. Noise pixels do not contribute to the desired texture of the image. The current filter can fail to remove noise for a few reasons: