Analysis of Different Image Enhancement and Feature Extraction Methods

This paper describes an image enhancement method for reliable image feature matching. Image features such as SIFT and SURF have been widely used in various computer vision tasks such as image registration and object recognition. However, the reliable extraction of such features is difficult in poorly illuminated scenes. One promising approach is to apply an image enhancement method before feature extraction, which preserves the original characteristics of the scene. We thus propose to use the Multi-Scale Retinex algorithm, which is aimed to emulate the human visual system and it provides more information of a poorly illuminated scene. We experimentally assessed various combinations of image enhancement (MSR, Gamma correction, Histogram Equalization and Sharpening) and feature extraction methods (SIFT, SURF, ORB, AKAZE) using images of a large variety of scenes, demonstrating that the combination of the Multi-Scale Retinex and SIFT provides the best results in terms of the number of reliable feature matches.

Automated summary

This paper describes an image enhancement method for reliable image feature matching. By applying an image enhancement method before feature extraction, the original characteristics of the scene can be preserved. Experimental results demonstrate that the combination of the Multi-Scale Retinex algorithm and SIFT method provides the best results in terms of the number of reliable feature matches.