In this article, we have reviewed the reasons why we (collectively) want to love or leave the venerable (but perhaps hoary) MSE. We have also reviewed emerging alternative signal fidelity measures and discussed their potential application to a wide variety of problems. The message we are trying to send here is not that one should abandon use of the MSE nor to blindly switch to any other particular signal fidelity measure. Rather, we hope to make the point that there are powerful, easy-to-use, and easy-to-understand alternatives that might be deployed depending on the application environment and needs. While we expect (and indeed, hope) that the MSE will continue to be widely used as a signal fidelity measure, it is our greater desire to see more advanced signal fidelity measures being used, especially in applications where perceptual criteria might be relevant. Ideally, the performance of a new signal processing algorithm might be compared to other algorithms using several fidelity criteria. Lastly, we hope that we have given further motivation to the community to consider recent advanced signal fidelity measures as design criteria for optimizing signal processing algorithms and systems. It is in this direction that we believe that the greatest benefit eventually lies.

Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures

This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

A fast and effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on a two-scale decomposition of an image into a base layer containing large scale variations in intensity, and a detail layer capturing small scale details. A novel guided filtering-based weighted average technique is proposed to make full use of spatial consistency for fusion of the base and detail layers. Experimental results demonstrate that the proposed method can obtain state-of-the-art performance for fusion of multispectral, multifocus, multimodal, and multiexposure images.

/pdf/image-fusion-with-guided-filtering-4bhdh8vaii.pdf

Image Fusion With Guided Filtering

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Pixel-level image fusion

A novel similarity based quality metric for image fusion

The recently synthesized two-dimensional (2D) MoSi2N4 material with a proper band gap and excellent environmental stability promises potential optoelectronic applications. On the basis of first-principles calculations and electron–phonon interaction theory, we systematically analyze the structural, optical, carrier distribution and transport, and photocatalytic water-splitting properties of pristine and strained MoSi2N4 monolayers. They are all found to be dynamically stable. Their optical absorption efficiencies are very high for photon energy larger than the respective band gap. Surprisingly, the optical absorption coefficient of tensile MoSi2N4 is as large as 106 cm–1 across 300–880 nm. To the best of our knowledge, this is the best among optoelectronic materials in the visible region. In addition, compressive strain results in spatial separation of photogenerated electrons and holes, which is beneficial for increasing their separation rates. Transport properties indicate that the lifetime and mean free path (MFP) of photogenerated electrons and holes are, respectively, on the scale of several femtoseconds and within 1.5 nm. Tensile strain notably increases them, becoming comparable to conventional semiconductor Si. Finally, pristine and strained MoSi2N4 monolayers are predicated to be able to photooxidize and photoreduce H2O with proper band edges and H2O adsorption capacities. These systematic characterizations not only deepen understanding of the physical and optoelectronic properties of MoSi2N4 but also indicate promising applications of MoSi2N4 in electronics, optoelectronics, and photocatalytic water splitting.

Strained MoSi2N4 Monolayers with Excellent Solar Energy Absorption and Carrier Transport Properties

A new algorithm is presented which deals with the problem of detecting small moving targets in infrared image sequences that also contain drifting and evolving clutter. Through development of models of the temporal behavior of the static background, target and cloud edge on a single pixel basis, the new algorithm employing the connecting line of the stagnation points (CLSP) of the temporal profile as the baseline is created and tested. The deviation of the temporal profile and its CLSP is analyzed and it is determined that the distribution of the residual temporal profile obtained by subtracting the baseline from the temporal profile can be modeled by a Gaussian distribution. The occurrences of the targets have intensity values significantly different to the distribution of the residual temporal profile. Unlike the conventional 3-D method, this new algorithm operates on the temporal profile in 1-D space, not in 3-D space, thus having a higher computational efficiency. Experiments with real IR image sequences have proved the validity of the new approach.

http://web.xidian.edu.cn/dlliu/files/20140113_093851.pdf

Temporal Profile Based Small Moving Target Detection Algorithm in Infrared Image Sequences

Moving target detection by nonlinear adaptive filtering on temporal profiles in infrared image sequences

Clutter plays a very important role in the area of human-in-the-loop target acquisition. A great deal of interest has recently been shown in defining clutter for electro-optical images. In spite of so many definitions available, no single clutter definition has been agreed on by the electro-optical community as being the best. We modify the structure similarity index, which is extensively used for image quality assessment, to be clutter metrics and call them target structure similarity measures (TSSIMs). Two different methods for calculating the TSSIM metrics are suggested. The TSSIMs of many images in Search/spl I.bar/2 dataset have been analyzed. Experimental results show the proposed metrics correlate very well to the detection probability of the observers.

Jianqi Zhang

Papers

A novel similarity based quality metric for image fusion

Strained MoSi2N4 Monolayers with Excellent Solar Energy Absorption and Carrier Transport Properties

Temporal Profile Based Small Moving Target Detection Algorithm in Infrared Image Sequences

Moving target detection by nonlinear adaptive filtering on temporal profiles in infrared image sequences

New metrics for clutter affecting human target acquisition