Full report of the process and results can be found in AnalysisReport.pdf
The goal of this project is to create hybrid image using the approach described in SIGGRAPH 2006 paper by Oliva, Torralba, and Schyns.
The steps for creating a hybrid image are as follows:
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Select two similar images: The first step is to select two images with similar content and features, as this makes it easier to combine them and produce a convincing hybrid image.
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Align the images: Resize and rotate the images such that the object on first image will be perfectly located on top of the object on the other image.
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Filter the images: Apply a low-pass filter to one image to extract the low-frequency content, and a high-pass filter to the other image to extract the high-frequency content.
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Experiment with weights: Experiment with different weights to control the relative dominance of the low-pass and high-pass filter.
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Combine the filtered images: Combine the filtered images by adding them, to produce the final hybrid image.
The final result of a hybrid image is a composite image that merges the low-frequency content from one image with the high-frequency content from another image. The low-frequency content provides the overall structure and the dominant features of the image, while the high-frequency content provides the fine details and sharp edges of the image.
| Final hybrid image | Final hybrid image scale |
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The Laplacian pyramid is a sequence of the difference between each level of the Gaussian pyramid and the upsampled version of the next level. The Laplacian pyramid represents the high-frequency content of the image, which corresponds to the fine details and sharp edges in the image. The Laplacian pyramid provides a multi-scale representation of the image's high-frequency content, where each level of the pyramid represents a different scale or level of detail of the image.
The purpose of image enhancement is to improve the visual quality of an image by adjusting its contrast, brightness, sharpness, and other attributes. Image enhancement techniques can be used to make an image clearer, more detailed, and easier to understand.
Histogram equalization is often considered a good method for contrast enhancement because it works by redistributing the intensities of the pixels in an image such that the resulting histogram is more uniform, which can result in an image with a higher dynamic range and improved contrast.
| Original image | Contrast enhancement |
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For this part I decided to use gamma correction on the S (Saturation) channel of HSV color space. The gamma correction function is defined by a single parameter, known as the gamma value, which determines the degree of the non-linearity. I'm aware that gamma function is not specifically designed for this purpose, but I'm really happy with how it turned out so I decided to share my results with you.
| Original image | Color enhancement |
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Color shifting refers to the process of adjusting the hue of an image. I decided to mask pixels that correspond to color on the A and B channels of LAB color space (e.g. >127 on A channel for red), and use a simple scalar multiplication to boost or weaken the color. Multiplying by a value greater than 1 on A channel boosts the red color without changing green pixels. The same holds for the B channel, multiplying by a value less than 1 boosts the yellow color without changing blue pixels.
| Original image | Color shift - more red | Color shift - less yellow |
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