We develop a method for automatic colorization of im- ages (or two-dimensional fields) in order to visualize pixel values and their local differences. In many applications, local differences in pixel values are as important as their values. For example, in topog- raphy, both elevation and slope often must be considered. Gradient- based value mapping (GBVM) is a technique for colorizing pixels based on value (e.g., intensity or elevation) and gradient (e.g., local differences or slope). The method maps pixel values to a color scale (either gray-scale or pseudocolor) in a manner that emphasizes gra- dients in the image while maintaining ordinal relationships of values. GBVM is especially useful for high-precision data, in which the num- ber of possible values is large. Colorization with GBVM is demon- strated with data from comprehensive two-dimensional gas chroma- tography (GCxGC), using both gray-scale and pseudocolor to visualize both small and large peaks, and with data from the Global Land One-Kilometer Base Elevation (GLOBE) Project, using gray- scale to visualize features that are not visible in images produced with popular value-mapping algorithms. © 2007 SPIE and

/pdf/gradient-based-value-mapping-for-pseudocolor-images-4koq5x10ld.pdf

2.1 – Basic Gray-Level Image Processing

Gradient-based value mapping for pseudocolor images

The exponential growth of deep learning networks has allowed us to tackle complex tasks, even in fields as complicated as medicine. However, using these models requires a large corpus of data for the networks to be highly generalizable and with high performance. In this sense, data augmentation methods are widely used strategies to train networks with small data sets, being vital in medicine due to the limited access to data. A clear example of this is magnetic resonance imaging in pathology scans associated with cancer. In this vein, we compare the effect of several conventional data augmentation schemes on the ResNet50 network for brain tumor detection. In addition, we included our strategy based on principal component analysis. The training was performed with the network trained from zeros and transfer-learning, obtained from the ImageNet dataset. The investigation allowed us to achieve an F1 detection score of 92.34%. The score was achieved with the ResNet50 network through the proposed method and implementing the learning transfer. In addition, it was also concluded that the proposed method is different from the other conventional methods with a significance level of 0.05 through the Kruskal Wallis test statistic. 

Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging

SAR image despeckling using deep CNN

Image resolution is determined by pixels per square inches. Pixel is defined as a small unit of an image. The resolution improvement of the sonar images still remains a problem and it is compensated by the interpolation techniques. This work helps to improve the resolution of the sonar images and thereby it makes target object recognition with higher accuracy. The super resolution algorithm of the proposed work uses sparse representation for enhancing the resolution. This gives sharper edges and textures to highlight the boundaries of the target object. The low resolution image is converted into high resolution image that is of high pixel density. The peak signal to noise ratio and Structural Similarity Index is the performance metric employed to assess the different interpolation techniques for resolution enhancement.

/pdf/super-resolution-enhancement-of-underwater-sonar-images-507o28wvl6.pdf

2.1 – Basic Gray-Level Image Processing

Citations

Gradient-based value mapping for pseudocolor images

Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging

SAR image despeckling using deep CNN

Super resolution enhancement of underwater sonar images

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