Evaluation of blurring and noise of a medical image using a digital phantom

Blurring and noise are an essential characteristic of a medical image on the imaging system. This study shows the characteristics of blurring and noise of a medical image using a digital phantom. A square-shaped digital phantom was produced with pixels that consist of black and white. The line profile was selected on a binary digital image. An image with noise added was generated and a corresponding line profile was also drawn. The degree of noise was increased using the gaussian noise value. The blurring images obtained by applying gaussian blur to a digital phantom was produced similarities to real images. Also, the degree of blurring was increased using the gaussian blur value. As noise increased, the standard deviation of pixels inside and background the object also increased. However, the boundary of the object was retained. As image blurring increased, the boundary of the object was not clearly distinguished. However, the standard deviation of pixels inside and background the object was retained. When extreme noise and blurring are increased, the resulting images are different. For adding noise, the shape is visually maintained. However, the blurred image does not maintain a square shape. Therefore, it is shown that blurring due to movement of object cannot maintain original form. In the image processing method, the reduction of noise is achieved through blur processing. The noise was reduced through blur processing in the image with noise. The degree of noise decreased, but the ambiguity of the boundary increased.

AN EFFECTIVE IMAGE DEBLURRING SCHEME USING CLUSTER BASED SPARSE REPRESENTATION

Sparse based representation is being used extensively for image restoration. The dictionary learningthrough patch extraction is central to the sparse based schemes. In the process of dictionary learning,a large number of patches will be extracted from high quality images and dictionary will be formed.Hence, over-complete dictionaries will be built. To overcome the complexity associated with overcompletedictionaries many schemes were proposed. Of them, the adaptive sparse domain is thepopular one. Many variations of adaptive sparse domain schemes were proposed. Selection of obviouspatches is common to all. In all these schemes, individual patches will be considered as the basic entityand will be used. This is the reason for the complexity involved in sparse representation. In this paper,to avoid the complexity, the patches are grouped according to the similarity among the patches. Inaddition to reduce the complexity the proposed cluster based scheme considers the self-similarity ofthe patches involved. Hence better performance with less complexity is possible with the proposedschemes. In the process of testing, in addition to uniform blur and Gaussian blur, a combination of thetwo blurs is also considered.

Analisis Retouching Foto Close Up Karya Petra Sinuraya Menggunakan Teknik Digital

This study aims to describe the concept and process of visualization of the close-up photo retouching technique by Petra Sinuraya. This research method is descriptive analytic research on the work. The subject of this type of research is Petra Sinuraya's close up photography retouching work. Data were analyzed descriptively analytic with percentage analysis using simple statistical procedures. Data obtained by using interview and documentation methods. The instrument was designed based on interview and documentation guidelines, and was developed based on situations that occurred in the field. The research was conducted by taking and selecting documents in the form of 10 pieces of art photos. The results showed that the close-up photo retouching process used by Petra Sinuraya was a digital technique by sharpening skills through the touch of tools available in Photoshop software. The role of composition in the retouching process is very important for client needs so that the photo looks more attractive in the final result. As for what Petra Sinuraya does in Close Up photo retouching is in various ways such as the Spot Hiling Brush for Smoothing the Skin, Burn and dodge tool for eye retouching, Dodge and Burn for Lightening / Darkening Contrast, filter noise and Gaussian blur - for flawless skin. and the Patch tool to enhance photos.

Vehicles Detection System at Different Weather Conditions

The importance of efficient vehicle detection (VD) is increased with the expansion of road networks and the number of vehicles in the Intelligent Transportation Systems (ITS). This paper proposes a system for detecting vehicles at different weather conditions such as sunny, rainy, cloudy and foggy days. The first step to the proposed system implementation is to determine whether the video’s weather condition is normal or abnormal. The Random Forest (RF) weather condition classification was performed in the video while the features were extracted for the first two frames by using the Gray Level Co-occurrence Matrix (GLCM). In this system, the background subtraction was applied by the mixture of Gaussian 2 (MOG 2) then applying a number of pre-processing operations, such as Gaussian blur filter, dilation, erosion, and threshold. The main contribution of this paper is to propose a histogram equalization technique for complex weather conditions instead of a Gaussian blur filter for improving the video clarity, which leads to increase detection accuracy. Based on the previous steps, the system defines each region in the frame expected to contain vehicles. Finally, Support Vector Machine (SVM) classifies the defined regions into a vehicle or not. As compared to the previous methods, the proposed system showed high efficiency of about 96.4% and ability to detect vehicles at different weather conditions.

Influence of Image Quality and Light Consistency on the Performance of Convolutional Neural Networks for Weed Mapping

Recent computer vision techniques based on convolutional neural networks (CNNs) are considered state-of-the-art tools in weed mapping. However, their performance has been shown to be sensitive to image quality degradation. Variation in lighting conditions adds another level of complexity to weed mapping. We focus on determining the influence of image quality and light consistency on the performance of CNNs in weed mapping by simulating the image formation pipeline. Faster Region-based CNN (R-CNN) and Mask R-CNN were used as CNN examples for object detection and instance segmentation, respectively, while semantic segmentation was represented by Deeplab-v3. The degradations simulated in this study included resolution reduction, overexposure, Gaussian blur, motion blur, and noise. The results showed that the CNN performance was most impacted by resolution, regardless of plant size. When the training and testing images had the same quality, Faster R-CNN and Mask R-CNN were moderately tolerant to low levels of overexposure, Gaussian blur, motion blur, and noise. Deeplab-v3, on the other hand, tolerated overexposure, motion blur, and noise at all tested levels. In most cases, quality inconsistency between the training and testing images reduced CNN performance. However, CNN models trained on low-quality images were more tolerant against quality inconsistency than those trained by high-quality images. Light inconsistency also reduced CNN performance. Increasing the diversity of lighting conditions in the training images may alleviate the performance reduction but does not provide the same benefit from the number increase of images with the same lighting condition. These results provide insights into the impact of image quality and light consistency on CNN performance. The quality threshold established in this study can be used to guide the selection of camera parameters in future weed mapping applications.

Single image super resolution based on multi-scale structure and non-local smoothing

In this paper, we propose a hybrid super-resolution method by combining global and local dictionary training in the sparse domain. In order to present and differentiate the feature mapping in different scales, a global dictionary set is trained in multiple structure scales, and a non-linear function is used to choose the appropriate dictionary to initially reconstruct the HR image. In addition, we introduce the Gaussian blur to the LR images to eliminate a widely used but inappropriate assumption that the low resolution (LR) images are generated by bicubic interpolation from high-resolution (HR) images. In order to deal with Gaussian blur, a local dictionary is generated and iteratively updated by K-means principal component analysis (K-PCA) and gradient decent (GD) to model the blur effect during the down-sampling. Compared with the state-of-the-art SR algorithms, the experimental results reveal that the proposed method can produce sharper boundaries and suppress undesired artifacts with the present of Gaussian blur. It implies that our method could be more effect in real applications and that the HR-LR mapping relation is more complicated than bicubic interpolation.

A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images

Super-resolution (SR) algorithms based on deep learning have dominated in various tasks, including medical imaging, street view surveillance and face recognition. In the remote sensing field, most of the current SR methods utilize the low-resolution (LR) images that directly bicubic downsampled the high-resolution (HR) images as not only train set but also test set, thus achieving high PSNR/SSIM scores but showing performance drop in application because the degradation model in remote sensing images is subjected to Gaussian blur with unknown parameters. Inspired by multi-task learning strategy, we propose a multiple-blur-kernel super-resolution framework (MSF), in which a multiple-blur-kernel learning module (MLM) optimizes the parameters of the network transferable and sensitive for SR procedures with different blur kernels. Besides, to simultaneously exploit the prior of the large-scale remote sensing images and recurrent information in a single test image, a class-feature capture module (CCM) and an unsupervised learning module (ULM) are leveraged in our framework. Extensive experiments show that our framework outperforms the current state-of-the-art SR algorithms in remotely sensed imagery SR with unknown Gaussian blur kernel.