Exploring Image Enhancement Techniques

What is the primary goal of histogram equalization?

• To reduce image size by compressing pixel data.

• To enhance image contrast by redistributing intensity values.

• To convert color images to grayscale.

• To apply a blur effect to the image.

Describe the process of applying a Gaussian filter for image smoothing.

• Applying a median filter instead of a Gaussian filter.

• Using a Laplacian filter for edge detection.

• The process of applying a Gaussian filter involves creating a Gaussian kernel, convolving it with the image, and replacing each pixel with a weighted average of its neighbors.

• Resizing the image before applying any filter.

What is the difference between linear and non-linear spatial filtering?

• Linear filtering is faster than non-linear filtering.

• Non-linear filtering only applies to color images.

• Linear filtering can only be applied to grayscale images.

• Linear filtering uses linear combinations of pixel values, while non-linear filtering uses non-linear operations.

Explain how edge detection can be achieved using the Sobel operator.

• Edge detection is performed by applying a Gaussian filter to the image.

• The Sobel operator uses color histograms to identify edges.

• Edge detection using the Sobel operator is achieved by convolving the image with Sobel kernels to compute gradient magnitudes.

• Edge detection is achieved by thresholding the pixel values directly.

What are the benefits of using contrast enhancement in images?

• Benefits of using contrast enhancement in images include improved visibility, better feature recognition, enhanced detail, and increased overall image quality.

• Decreased image size

• Lower resolution

• Increased color saturation

How does histogram equalization affect the overall brightness of an image?

• Histogram equalization decreases the overall brightness of an image.

• Histogram equalization has no effect on the brightness of an image.

• Histogram equalization only affects the contrast, not the brightness.

• Histogram equalization can increase the overall brightness of an image by redistributing pixel intensities.

List two common methods for image smoothing and their applications.

• Color Correction - used for adjusting color balance

• Sharpening - used for enhancing edges in images

• 1. Gaussian Blur - used for noise reduction and softening images; 2. Median Filtering - used for removing salt-and-pepper noise.

• Histogram Equalization - used for contrast adjustment

What is the purpose of using a Laplacian filter in edge detection?

• To blur the entire image for a smoother appearance.

• To highlight regions of rapid intensity change in an image.

• To enhance color saturation in an image.

• To reduce noise by averaging pixel values.

How can contrast enhancement improve the visibility of features in an image?

• It blurs the details to create a softer look.

• It reduces the overall brightness of the image.

• Contrast enhancement improves visibility by increasing the difference between light and dark areas, highlighting features and details.

• It eliminates all colors to focus on shapes.

What role does the kernel play in spatial filtering techniques?

• The kernel stores pixel values for later use.

• The kernel adjusts the brightness of the image.

• The kernel is responsible for image compression.

• The kernel defines the filter's behavior in spatial filtering by convolving with pixel values.