Performance Degraded by the Sensor Noise at Pixel Level Image Fusion

Abstract

Remote sensing is defined as obtaining information about a Performance metrics for measuring absolute degradation and their gain in fused image quality are proposed when fusing noisy input modalities. This considers fusion of noise patterns, is also developed and used to evaluate the perceptual effect of noise corrupting homogenous image regions (i.e. areas with no salient features). These metrics are employed to compare the performance of different image fusion methodologies and feature selection/information fusion strategies operating under noisy input conditions. The aim of this paper is to define appropriate metrics which measure the effects of input sensor noise on the performance of image fusion systems.' noisy fusion'' metrics are developed and used, in the first two scenarios, to measure the effects of additive sensor noise on the performance of several signal-level image fusion algorithms operating across a range of input signal-to-noise ratio (SNR) values.

An Image Fusion Technique Based on Hadamard Transform and HVS

“…The concept of image fusion has been used in a wide variety of applications like medicine, remote sensing, machine vision, automatic change detection, biometrics, robotics, microscopic vision etc. An image fusion algorithm [1][2][3][4][5][6] should require entire salient information contained in the input images. Fusion process should not introduce any artifacts or inconsistencies which can distract images.…”

Fusion of Multispectral and Panchromatic Data using Regionally Weighted Principal Component Analysis and Wavelet

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