Super Resolution with Deep Learning for Image Recognition

用于图像识别的深度学习超分辨率

• 批准号: 2104357
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2104357
• 关键词: Super; Resolution; Deep; Learning; Image

Deep learning is finding several applications in the automated recognition and identification of objects in large datasets. A particularly active research area is its application to super resolution in biomedical imaging. A typical example is the ability to produce high resolution images of cell structure by training a machine to correct blurred images by adapting image processing protocols to optimise the recovered image against a corresponding high quality image. Having "learned" the processing strategy with a wide variety of cell images and optical conditions, a blind test can then be performed on a blurred image for which there is no corresponding high quality image. The machine is able to "analyse", adapt, optimise and apply a processing strategy to recover a high quality image based on what it has previously learned using training images which are similar to the object under test. The applicants and Leonardo are interested in understanding how the same methodology can be applied to object recognition in an airborne of land-based scenario. The problem is characterised by several practical considerations:- There is more than one type of object to detect.- The background terrain is varied.- The camera (2D or 3D) provides a limited spatial resolution determined by the lens system (dependant on optical aberrations) and the sampling resolution (determined by the pixel resolution and pitch of the focal plane array). These determine the angular resolution of the camera. For 3D imaging the range resolution should be considered. The question then is: can the image be improved to approach the diffraction limit?- The object will usually need to be tracked and therefore the output should be at video rates. This also implies that the background is also variable during the track.- At long range atmospheric turbulence will distort the image in a varying manner. This means that the blurred image is continuously changing even for a stationary object.PROJECT AIMThe aim of the study is two-fold:1. Super Resolution of Optical Systems. Determine the processing protocols necessary for a deep learning approach to object recognition. One approach is to adopt a similar computational scheme as that described in [1] for super resolution imaging of cells but changing the target set, tags and training database to fit a terrain-based, object recognition problem. For example, a database comprising high angular resolution images of vehicles taken at short range might be used to train a machine to recognise a vehicle in a class of vehicles whose images are taken at long range, where the finer detail is not well resolved and limits the use of shape and contour recognition. 2. Image Recognition and Identification Through Turbulence. In this application the principle is very similar to that described in [1]. A training database comprising blurred images of objects (taken through turbulence) and the corresponding blur-free image (taken through very low level turbulence) is used to train a machine to recognise a blurred image . The protocol is then used on a blurred image of an object not included in the database to conduct a blind test. The difference between this and [1] is that the blurring will change over time and therefore several blurred images will be associated with a single blur-free image. The key questions are: what level of turbulence can be tolerated, to what extent does light level affect the performance (e.g. dusk and dawn) and what is the processing speed.REFERENCES [11] Y. Rivenson, Z. Goroc, H. Gunaydin, Y. Zhang, H. Wang, and A. Ozcan, "Deep Learning Microscopy", Optica, vol. 4, no. 11, 1437-1443, 2017.

深度学习在自动识别和识别大型数据集中的对象方面有几个应用。一个特别活跃的研究领域是其在生物医学成像中的超分辨率应用。一个典型的例子是通过训练机器来产生细胞结构的高分辨率图像的能力，以通过调整图像处理协议来校正模糊图像，以针对相应的高质量图像优化恢复的图像。在“学习”了具有各种各样的细胞图像和光学条件的处理策略之后，然后可以对不存在对应的高质量图像的模糊图像执行盲测试。该机器能够“分析”，适应，优化和应用处理策略，以恢复高质量的图像，基于它以前使用与测试对象相似的训练图像所学到的内容。申请人和列奥纳多感兴趣的是理解如何将相同的方法应用于机载或陆基场景中的对象识别。该问题的特点是有几个实际考虑因素：-需要检测的对象类型不止一种。-背景地形是多种多样的。相机（2D或3D）提供由透镜系统（取决于光学像差）确定的有限空间分辨率和采样分辨率（由像素分辨率和焦平面阵列的间距确定）。这些决定了相机的角分辨率。对于3D成像，应考虑距离分辨率。那么问题来了：图像能被改善到接近衍射极限吗？对象通常需要被跟踪，因此输出应该是视频速率。这也意味着背景在跟踪期间也是可变的。在远距离大气湍流将扭曲的图像以不同的方式。这意味着即使对于静止的物体，模糊的图像也是连续变化的。光学系统的超分辨率。确定对象识别的深度学习方法所需的处理协议。一种方法是采用与[1]中描述的用于细胞的超分辨率成像的计算方案类似的计算方案，但是改变目标集、标签和训练数据库以适应基于地形的对象识别问题。例如，包括在短距离处拍摄的车辆的高角分辨率图像的数据库可以用于训练机器以识别其图像在长距离处拍摄的一类车辆中的车辆，其中较精细的细节没有被很好地解析并且限制了形状和轮廓识别的使用。2.通过湍流的图像识别和鉴定。在本申请中，原理与[1]中描述的原理非常相似。包括对象的模糊图像（通过湍流拍摄）和对应的无模糊图像（通过非常低水平的湍流拍摄）的训练数据库用于训练机器以识别模糊图像。然后将该协议用于数据库中不包括的对象的模糊图像，以进行盲测。这与[1]之间的区别在于模糊将随时间变化，因此多个模糊图像将与单个无模糊图像相关联。关键问题是：可以容忍什么水平的湍流，光水平在多大程度上影响性能（例如黄昏和黎明）以及处理速度是什么。Rivenson，Z.戈罗克湾Gunaydin，Y. Zhang，H. Wang和A. Ozcan，“深度学习显微镜”，Optica，第4卷，第11期，1437-1443，2017年。