# Review of Algorithms for Compressive Sensing of Images

## TL;DR

This paper reviews classical algorithms for image compressive sensing, emphasizing Total variation methods, with practical insights for LiDAR applications, noise simulation, and algorithm comparison for beginners.

## Contribution

It provides a comprehensive beginner-friendly overview, theoretical background, noise considerations, and standardized comparison of algorithms for compressive sensing of images.

## Key findings

- Total variation methods are effective for image compressive sensing.
- Simulated LiDAR noise impacts algorithm performance.
- Standardized comparisons aid in selecting suitable algorithms.

## Abstract

We provide a comprehensive review of classical algorithms for compressive sensing of images, focused on Total variation methods, with a view to application in LiDAR systems. Our primary focus is providing a full review for beginners in the field, as well as simulating the kind of noise found in real LiDAR systems. To this end, we provide an overview of the theoretical background, a brief discussion of various considerations that come in to play in compressive sensing, and a standardized comparison of off-the-shelf methods, intended as a quick-start guide to choosing algorithms for compressive sensing applications.