Motion blur in digital images - analys, detection and correction of motion blur in photogrammetry

Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This thesis proves the negative affect that blurred images have on photogrammetric processing. It shows that small amounts of blur do have serious impacts on target detection and that it slows down processing speed due to the requirement of human intervention. Larger blur can make an image completely unusable and needs to be excluded from processing. To exclude images out of large image datasets an algorithm was developed. The newly developed method makes it possible to detect blur caused by linear camera displacement. The method is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of additional images. However, the calculated blur value named SIEDS (saturation image edge difference standard-deviation) on its own does not provide an absolute number to judge if an image is blurred or not. To achieve a reliable judgement of image sharpness the SIEDS value has to be compared to other SIEDS values of the same dataset. This algorithm enables the exclusion of blurred images and subsequently allows photogrammetric processing without them. However, it is also possible to use deblurring techniques to restor blurred images. Deblurring of images is a widely researched topic and often based on the Wiener or Richardson-Lucy deconvolution, which require precise knowledge of both the blur path and extent. Even with knowledge about the blur kernel, the correction causes errors such as ringing, and the deblurred image appears muddy and not completely sharp. In the study reported in this paper, overlapping images are used to support the deblurring process. An algorithm based on the Fourier transformation is presented. This works well in flat areas, but the need for geometrically correct sharp images for deblurring may limit the application. Another method to enhance the image is the unsharp mask method, which improves images significantly and makes photogrammetric processing more successful. However, deblurring of images needs to focus on geometric correct deblurring to assure geometric correct measurements. Furthermore, a novel edge shifting approach was developed which aims to do geometrically correct deblurring. The idea of edge shifting appears to be promising but requires more advanced programming

3D modeling of ultra-high-resolution UAV imagery using low-cost photogrammetric software and structure from motion

The availability of advanced low-cost unmanned aerial systems (UASs), aftermarket applications, and a competitive market for processing software have provided researchers new opportunities for employing high resolution remote sensing in research. The UAS allows for the capture of close range aerial imagery that can then be used to generate dense point clouds using structure from motion (SfM). A variety of digital products can be created form these dense point clouds such as three-dimensional models, digital elevation models (DEMs), digital surface models (DSMs) and orthomosaics. This dissertation looks at methods and accuracies associated with the creation of digital mapping products from dense point clouds generated from imagery captured by two low-cost off the shelf UASs. The UASs were used to capture imagery over a 2-hectare vineyard in the Uwharrie mountains of North Carolina. Aspects of imagery collection, such as altitude, ground control, camera types, flight paths, and target styles, were investigated for their impacts on accuracy. Thirty-one ground control points were created in the vineyard using a survey grade GNSS receiver and total station for use in georeferencing. The number of ground control points used for georeferencing were reduced until a significant difference in accuracy was found using t-tests. Five ground control points were shown to be the least amount of ground control needed before accuracy began to change significantly. Four flight altitudes were tested with 80-meters generating the least level of error. Orthomosaics created from structure from motion and imagery collected using a global shutter 20-megapixel had total RMS errors between 2-4 cm

Video Magnification for Structural Analysis Testing

The goal of this thesis is to allow a user to see minute motion of an object at different frequencies, using a computer program, to aid in vibration testing analysis without the use of complex setups of accelerometers or expensive laser vibrometers. MIT’s phase-based video motion processing ­was modified to enable modal determination of structures in the field using a cell phone camera. The algorithm was modified by implementing a stabilization algorithm and permitting the magnification filter to operate on multiple frequency ranges to enable visualization of the natural frequencies of structures in the field. To implement multiple frequency ranges a new function was developed to implement the magnification filter at each relevant frequency range within the original video. The stabilization algorithm would allow for a camera to be hand-held instead of requiring a tripod mount. The following methods for stabilization were tested: fixed point video stabilization and image registration. Neither method removed the global motion from the hand-held video, even after masking was implemented, which resulted in poor results. Specifically, fixed point did not remove much motion or created sharp motions and image registration introduced a pulsing effect. The best results occurred when the object being observed had contrast from the background, was the largest feature in the video frame, and the video was captured from a tripod at an appropriate angle. The final program can amplify the motion in user selected frequency bands and can be used as an aid in structural analysis testing

Depth and IMU aided image deblurring based on deep learning

Abstract. With the wide usage and spread of camera phones, it becomes necessary to tackle the problem of the image blur. Embedding a camera in those small devices implies obviously small sensor size compared to sensors in professional cameras such as full-frame Digital Single-Lens Reflex (DSLR) cameras. As a result, this can dramatically affect the collected amount of photons on the image sensor. To overcome this, a long exposure time is needed, but with slight motions that often happen in handheld devices, experiencing image blur is inevitable. Our interest in this thesis is the motion blur that can be caused by the camera motion, scene (objects in the scene) motion, or generally the relative motion between the camera and scene. We use deep neural network (DNN) models in contrary to conventional (non DNN-based) methods which are computationally expensive and time-consuming. The process of deblurring an image is guided by utilizing the scene depth and camera’s inertial measurement unit (IMU) records. One of the challenges of adopting DNN solutions is that a relatively huge amount of data is needed to train the neural network. Moreover, several hyperparameters need to be tuned including the network architecture itself. To train our network, a novel and promising method of synthesizing spatially-variant motion blur is proposed that considers the depth variations in the scene, which showed improvement of results against other methods. In addition to the synthetic dataset generation algorithm, a real blurry and sharp dataset collection setup is designed. This setup can provide thousands of real blurry and sharp images which can be of paramount benefit in DNN training or fine-tuning