發(fā)布時間：2018-07-11 17:29

  本文選題：復(fù)用成像 + 濾色陣列��； 參考：《北京交通大學(xué)》2017年博士論文

【摘要】：復(fù)用成像是指用單個傳感器實現(xiàn)同時采樣多個成像維度(如,空間、時間、光譜和明度),而具體需要同時采樣哪些成像維度是應(yīng)用相關(guān)的。復(fù)用成像的實例包括使用濾色陣列(color filter array,CFA)的彩色成像,使用空域變曝光(spatially varying exposures,SVE)技術(shù)的高動態(tài)范圍(high dynamic range,HDR)成像,以及多幅圖像超分辨率(super-resolution)等。在過去的幾十年間,該問題的研究已經(jīng)取得了較大的進(jìn)展。然而,仍有一些問題需要進(jìn)一步考慮。第一,用于復(fù)用成像的傳感器大多使用正方形像素和規(guī)則布局。然而,以往的研究表明動物視網(wǎng)膜的不規(guī)則布局有利于得到高質(zhì)量圖像。所以,使用不規(guī)則像素布局進(jìn)行復(fù)用成像值得研究。第二,大多數(shù)針對復(fù)用成像的像素設(shè)計忽略了后續(xù)重建算法的特性。為了增強某一類重建算法的性能,可以專門針對該類算法設(shè)計像素分配方式,并在設(shè)計過程中充分考慮重建過程。本文針對上述問題進(jìn)行研究,取得了以下創(chuàng)新性的成果:(1)本文提出彭羅斯(Penrose)像素布局用于圖像去馬賽克(demosaicking)問題,同時提出Ammann-Beenker像素布局用于圖像超分辨率和去馬賽克問題。這兩個像素布局都是非周期且不規(guī)則的,可以均勻三著色,并且僅使用兩種形狀的像素。這些特性使得它們在制造成像傳感器方面優(yōu)于其他不規(guī)則像素布局。我們在圖像超分辨率和去馬賽克問題上測試這兩個不規(guī)則像素布局的性能。實驗結(jié)果表明彭羅斯和Ammann-B eenker像素布局優(yōu)于正方形像素布局。(2)本文提出彭羅斯像素布局用于基于SVE技術(shù)的HDR成像。這使得彭羅斯像素布局在曝光和像素分配這兩個方面都是非周期的。由于彭羅斯像素布局是不規(guī)則和非周期的,現(xiàn)有的HDR重建方法不再適用。我們以高斯混合模型(Gaussian mixture model,GMM)為正則化項,開發(fā)出一個新的HDR重建方法。大量實驗表明彭羅斯像素布局有利于緩解重建HDR圖像分辨率降低的問題。(3)基于頻域結(jié)構(gòu),本文提出一個針對頻域選擇去馬賽克算法的自動CFA設(shè)計方法。然后,我們將該方法擴展用于設(shè)計使用全色像素的高光敏感CFA.該設(shè)計方法使用一個數(shù)學(xué)模型進(jìn)行求解,并且是全自動的。具體地,我們將使用全色像素的高光敏感CFA設(shè)計形式化為多目標(biāo)優(yōu)化問題,它同時最大化對光譜混疊的健壯性和全色像素的比例。在低光照和正常光照數(shù)據(jù)集上的實驗結(jié)果驗證了所提出設(shè)計方法的優(yōu)越性。(4)給定一個合適的字典,本文提出一個有理論保證的針對稀疏表示去馬賽克算法的CFA設(shè)計方法。我們將CFA設(shè)計看作包含CFA物理可制造約束的互相干性(mutual coherence)最小化問題。這些約束導(dǎo)致已有的求解互相干性最小化的方法不再適用。基于廣義分式規(guī)劃,我們提出一個具有收斂性保證的求解該問題的方法。在標(biāo)準(zhǔn)數(shù)據(jù)集上的大量實驗驗證了該設(shè)計方法的優(yōu)越性。
[Abstract]:Multiplexing imaging refers to the simultaneous sampling of multiple imaging dimensions (e.g., space, time, spectrum and brightness) using a single sensor, and which imaging dimensions need to be sampled simultaneously are application-related. Examples of multiplexing imaging include color imaging using filter array (color filter array, high dynamic range (high dynamic imaging using spatial variable exposure (spatially varying) technique, and multi-image super-resolution (super-resolution). In the past few decades, great progress has been made in the study of this problem. However, there are still some issues that need further consideration. First, most sensors for multiplexing imaging use square pixels and regular layouts. However, previous studies have shown that the irregular layout of the animal retina is conducive to high-quality images. Therefore, the use of irregular pixel layout for multiplexing imaging is worth studying. Second, most pixel designs for multiplexed images ignore the characteristics of subsequent reconstruction algorithms. In order to enhance the performance of a certain kind of reconstruction algorithm, we can design a pixel allocation method for this kind of algorithm, and take the reconstruction process into full consideration in the design process. In this paper, the following innovative results are obtained: (1) Penrose pixel layout is proposed for image de-mosaic (demosaicking) problem, and Ammann-Beenker pixel layout for image super-resolution and de-mosaics problem is proposed. Both pixel layouts are aperiodic and irregular, can be evenly colored, and use only two shapes of pixels. These features make them superior to other irregular pixel layouts in manufacturing imaging sensors. We tested the performance of these two irregular pixel layouts on image superresolution and de-mosaics. Experimental results show that Penrose and Ammann-B eenker pixel layout is superior to square pixel layout. (2) this paper presents Penrose pixel layout for eenker imaging based on SVE technology. This makes the Penrose pixel layout aperiodic in both exposure and pixel allocation. Because the Penrose pixel layout is irregular and aperiodic, the existing HDR reconstruction method is no longer applicable. A new method of Gao Si reconstruction is developed using Gaussian mixture model (GMM) as regularization term. A large number of experiments show that the Penrose pixel layout is helpful to alleviate the problem of low resolution of reconstructed HDR images. (3) based on the frequency domain structure, this paper proposes an automatic CFA design method for the frequency-domain selective de-mosaics algorithm. Then, we extend the method to design a highlight sensitive CFAs using panchromatic pixels. The design method is solved by a mathematical model and is fully automatic. Specifically, we formalize the high-light-sensitive CFA design using panchromatic pixels as a multi-objective optimization problem, which maximizes both the robustness of spectral aliasing and the ratio of panchromatic pixels. Experimental results on low and normal illumination datasets demonstrate the superiority of the proposed design method. (4) given a suitable dictionary, this paper presents a theoretically guaranteed CFA design method for sparse representation de-mosaics algorithm. We consider CFA design as a coherent (mutual coherence) minimization problem with physical manufacturability constraints. These constraints make the existing methods for minimizing the coherence no longer applicable. Based on generalized fractional programming, we propose a method for solving this problem with convergence guarantee. A large number of experiments on the standard data set verify the superiority of the design method.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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