You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review on the Extraction of Quantitative Retinal Microvascular Image Feature 
Chapter Title:  Retinal Image Restoration. 
1. Abstract of Cited Paper (BIB001):  Retinal imaging plays a key role in the diagnosis and management of ophthalmologic disorders, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. Because of the acquisition process, retinal images often suffer from blurring and uneven illumination. This problem may seriously affect disease diagnosis and progression assessment. Here we present a method for color retinal image restoration by means of multichannel blind deconvolution. The method is applied to a pair of retinal images acquired within a lapse of time, ranging from several minutes to months. It consists of a series of preprocessing steps to adjust the images so they comply with the considered degradation model, followed by the estimation of the point-spread function and, ultimately, image deconvolution. The preprocessing is mainly composed of image registration, uneven illumination compensation, and segmentation of areas with structural changes. In addition, we have developed a procedure for the detection and visualization of structural changes. This enables the identification of subtle developments in the retina not caused by variation in illumination or blur. The method was tested on synthetic and real images. Encouraging experimental results show that the method is capable of significant restoration of degraded retinal images. 
2. Abstract of Cited Paper (BIB002):  Retinal images are essential clinical resources for the diagnosis of retinopathy and many other ocular diseases. Because of improper acquisition conditions or inherent optical aberrations in the eye, the images are often degraded with blur. In many common cases, the blur varies across the field of view. Most image deblurring algorithms assume a space-invariant blur, which fails in the presence of space-variant (SV) blur. In this work, we propose an innovative strategy for the restoration of retinal images in which we consider the blur to be both unknown and SV. We model the blur by a linear operation interpreted as a convolution with a point-spread function (PSF) that changes with the position in the image. To achieve an artifact-free restoration, we propose a framework for a robust estimation of the SV PSF based on an eye-domain knowledge strategy. The restoration method was tested on artificially and naturally degraded retinal images. The results show an important enhancement, significant enough to leverage the images' clinical use. 
3. Abstract of Cited Paper (BIB003):  Retinal images often suffer from blurring which hinders disease diagnosis and progression assessment. The restoration of the images is carried out by means of blind deconvolution, but the success of the restoration depends on the correct estimation of the point-spread-function (PSF) that blurred the image. The restoration can be space-invariant or space-variant. Because a retinal image has regions without texture or sharp edges, the blind PSF estimation may fail. In this paper we propose a strategy for the correct assessment of PSF estimation in retinal images for restoration by means of space-invariant or space-invariant blind deconvolution. Our method is based on a decomposition in Zernike coefficients of the estimated PSFs to identify valid PSFs. This significantly improves the quality of the image restoration revealed by the increased visibility of small details like small blood vessels and by the lack of restoration artifacts. 
4. Abstract of Cited Paper (BIB004):  In comparison with the standard three-channel colour images, spectral retinal images provide more detailed information about the structure of the retina. However, the availability of spectral retinal images for the research and development of image analysis methods is limited. In this paper, we propose two approaches to reconstruct spectral retinal images based on common RGB images. The approaches make use of fuzzy c-means clustering to perform quantization of the image data, and the radial basis function network to learn the mapping from the three-component color representation to the spectral space. The dissimilarities between the reconstructed spectral images and the original ones are evaluated on a retinal image set with spectral and RGB images, and by using a standard spectral quality metric. The experimental results show that the proposed approaches are able to reconstruct spectral retinal images with a relatively high accuracy.

Literature Review:
Image restoration can be defined as recovering or reconstructing a degraded image utilizing the earlier concept of image degradation phenomenon. The techniques of restoring an image are designed towards modeling the degradation and using the reverse procedures to regain the real image. In fundus retinal image analysis restoration process plays a vital role to avoid the blurring and uneven illumination due to the image acquisition process. The authors of BIB002 proposed a technique for the restoration of the color retinal image through multichannel blind deconvolution. Basically, this technique is designed by composing image registration, uneven illumination compensation, and segmentation that had been validated by applying on both synthetic and original retinal image. It was experienced that this method is able to restore the degraded retinal image and can detect and picturize the structural changes of the retinal image too BIB002 . Fundus eye image can be degraded with blur due to the inappropriate acquisition or congenital optical shedding in the eye. The restoration process is either space-invariant or space-variant and maximum existing algorithm for image deblurring can deal with the space-invariant blur but cannot work with space-variant (SV) blur image BIB003 . The authors of BIB003 BIB001 presented a retinal image restoring algorithm that can deal with both unknown and space-variant blur and formulate this algorithm and they shaped the blur, interpreting linear operation for convolution with Point-Spread Function (PSF) that changes with the position in the image. The study of BIB004 also presented a method to obtain a true estimation of Point-Spread Function (PSF) for restoring retinal image through space-invariant or space-invariant blind deconvolution based on the decomposition in Zernike coefficient of the estimated PFSs to determine the actual PSFs. Figure 10 shows the original and restored fundus retinal image. The study of BIB005 designed a method to recover the spectral retinal image of the common RGB image using fuzzy c-means clustering for quantizing the data of image and radial function network for learning the mapping from RGB representation to the spectral space. And authors of BIB005 used a spectral quality metric to evaluate their experiment result compared with a set of retinal images having both spectral and RGB image and found the accuracy of their output relatively high.