Image Processing — Summer 2024

Define : Sampling, Quantization, Resolution

Explain the concept of pixel adjacency and how it is used in connectivity analysis. Provide an example of a practical problem where adjacency connectivity is used.

Explain fundamental steps and objective of each step in digital image processing with proper diagram.

Explain the concept of noise models in image restoration. Provide examples of different types of noise that can affect digital images

Explain Adaptive Filters for image restoration in brief.

Provide a comprehensive overview of various noise models that can corrupt digital images, including Gaussian noise, salt-and-pepper noise, and Poisson noise. Discuss their characteristics and sources.

Describe image restoration process with block diagram and explain noisemodels.

Explain RGB color model in brief.

Differentiate: lossy image compression vs lossless image compression.

List out highpass filters used in sharpening image in frequency domain. Explain each in brief.

Explain the concept of error-free compression in image processing and how it differs from lossy compression.

List out color models. Explain HIS color model in brief.

List out lowpass filters used in smoothing image in frequency domain. Explain each in brief.

Explain the concept of image negatives in image processing. How does it alter the appearance of an image, and when is it useful?

Explain the concept of linear filters in spatial filtering.

Explain the process of histogram equalization for image enhancement. Highlight its benefits and potential limitations.

Describe the fundamental idea behind log transformations for gray level image enhancement. Provide an example scenario where log transformations are applicable.

Explain the concept of order-statistics filters in spatial filtering.

Derive the laplacian operator for image sharpening in spatial domain and show its usage.1

Briefly explain edge linking in image segmentation. How does it contribute to the process of identifying object boundaries?

Discuss the role of image segmentation in object recognition and image understanding. Provide examples of real-world applications where accurate segmentation is crucial.

Describe image pyramid technique.

Explain multiresolution expansion using wavelet function.

Discuss Haar transform in detail.

Explain adaptive thresholding in brief. List out theiradvantages over fixed thresholding methods.