Image Processing Techniques

Image Restoration and Reconstruction

• Image restoration involves fixing damaged images.
• Image reconstruction can include text, logo, and object removal.
• Inpainting reconstructs lost parts of an image by looking at the non-damaged regions.
• A mask image has pixel intensity values, with some being zero and others being non-zero where damage is located.
• The damaged pixels are set as a mask.
• The skimage.restoration module can be used, specifically importing the inpaint function.
• A mask is obtained using get_mask(defect_image).
• Inpainting is applied with inpaint.inpaint_biharmonic(defect_image, mask, channel_axis=-1).
• The defect and restored versions can be displayed with show_image.
• White pixels in a mask image indicate damaged areas.
• The code provides a function get_mask(image) to create a mask with three defect regions, setting specific pixel ranges.
• Image noise refers to random variations of brightness or color information, shown as grainy or pixelated disturbances.
• Noise can result from low light conditions, high ISO settings, and sensor limitations.
• Noise consists of brightness and color variations that don't correspond to reality.

Applying and Reducing Noise with Denoising Types

• The skimage.util library can import the random_noise function to apply noise.
• Applying random_noise(dog_image) adds noise to an image.
• Salt and pepper is a common type of noise.
• Denoising aims to reduce noise in images.
• Different denoising types include Total Variation (TV), Bilateral, Wavelet, and Non-local means denoising.
• Total variation filter denoising can be implemented with skimage.restoration and denoise_tv_chambolle.
• The line of code, denoised_image = denoise_tv_chambolle(noisy_image, weight=0.1, channel_axis=-1) applies TV denoising.
• A Bilateral filter can be applied using skimage.restoration and the denoise_bilateral function.
• The line of code denoised_image = denoise_bilateral(noisy_image, channel_axis=-1) demonstrates the use of a Bilateral filter.
• The resulting image from a Bilateral filter preserves the edges and is less smooth than the one from TV filter.

Superpixels and Image Segmentation

• Segmentation partitions images into regions (segments).
• The purpose of segmentation is to simplify or change the representation into something easier to analyze.
• Segmentation helps isolate elements, such as tumors in computed tomography or faces in an image.
• A single pixel is not a natural representation.
• Superpixels are bigger regions or grouped pixels.
• Superpixels are connected pixels with similar colors or gray levels.
• Superpixels carry more meaning than individual pixels, they offer more meaningful regions and computational efficiency.
• Supervised thresholding requires specifying a threshold value, while Unsupervised thresholding does not require prior knowledge and automatically subdivides images into regions and can use algorithms like Otsu.
• Simple Linear Iterative Clustering (SLIC) is an unsupervised segmentation method.
• SLIC segments the image using a machine learning algorithm called K-Means clustering.
• SLIC separates pixel values into a predefined number of sub-regions.
• With SLIC, modules from skimage.segmentation and skimage.color are imported using the key words slic and label2rgb.
• The slic() function obtains segments.
• Segments are put on top of original images to compare.
• The line of code segmented_image = label2rgb(segments, image, kind='avg') puts segments on top of the original image to compare.
• Using more segments refines the segmentation, with slic(image, n_segments=300) creating 300 regions.

Finding Contours in Images

• A contour is a closed shape of points or line segments that represent the boundaries of objects
• Contours can be used to measure the size of objects, classify shapes, and determine the quantity of objects.
• Applying thresholding or using edge detection produces a binary image.
• Transforming the image to a 2D grayscale is part of contour detection.
• Grayscale images can be achieved using the line of code image = color.rgb2gray(image)
• Transforming the image to a binary version is also part of contour detection.
• The value for threshold is obtained using code like thresh = threshold_otsu(image)
• Thresholding is applied using the line of code, thresholded_image = image > thresh.
• Contours at a constant value of 0.8 are defined.
• The line of code, contours = measure.find_contours(thresholded_image, 0.8) finds contours.
• The level value varies from 0 to 1.
• The methodology becomes more sensitive to detecting contours the closer you are to a value of 1.
• Steps to spotting contours: make the image grayscale, obtain the optimal threshold value, apply thresholding to get a binary image, and find contours at a constant value of 0.8.
• Contours are a list of (n,2) - ndarrays.
• Each contour is a list of (n,2)-ndarrays, representing coordinates of the points along line segments.

Questions covering image inpainting, restoration, noise, and contour finding using scikit-image. It touches upon techniques like biharmonic inpainting, masking, and thresholding. It also covers noise reduction and image enhancement.