Image resizing using saliency strength map and seam carving for white blood cell analysis

<p>Abstract</p> <p>Background</p> <p>A new image-resizing method using seam carving and a Saliency Strength Map (SSM) is proposed to preserve important contents, such as white blood cells included in blood cell images.</p> <p>Methods</p> <p>To apply seam carving to cell images, a SSM is initially generated using a visual attention model and the structural properties of white blood cells are then used to create an energy map for seam carving. As a result, the energy map maximizes the energies of the white blood cells, while minimizing the energies of the red blood cells and background. Thus, the use of a SSM allows the proposed method to reduce the image size efficiently, while preserving the important white blood cells.</p> <p>Results</p> <p>Experimental results using the PSNR (Peak Signal-to-Noise Ratio) and ROD (Ratio of Distortion) of blood cell images confirm that the proposed method is able to produce better resizing results than conventional methods, as the seam carving is performed based on an SSM and energy map.</p> <p>Conclusions</p> <p>For further improvement, a faster medical image resizing method is currently being investigated to reduce the computation time, while maintaining the same image quality.</p

Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions

Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datasets are unavailable for training deep learning models in the image retargeting tasks. As a result, we present a new supervised approach for training deep learning models. We use the original images as ground truth and create inputs for the model by resizing and cropping the original images. A second challenge is generating different image sizes in inference time. However, regular convolutional neural networks cannot generate images of different sizes than the input image. To address this issue, we introduced a new method for supervised learning. In our approach, a mask is generated to show the desired size and location of the object. Then the mask and the input image are fed to the network. Comparing image retargeting methods and our proposed method demonstrates the model's ability to produce high-quality retargeted images. Afterward, we compute the image quality assessment score for each output image based on different techniques and illustrate the effectiveness of our approach.Comment: 18 pages, 5 figure

Analysis of adversarial attacks against CNN-based image forgery detectors

With the ubiquitous diffusion of social networks, images are becoming a dominant and powerful communication channel. Not surprisingly, they are also increasingly subject to manipulations aimed at distorting information and spreading fake news. In recent years, the scientific community has devoted major efforts to contrast this menace, and many image forgery detectors have been proposed. Currently, due to the success of deep learning in many multimedia processing tasks, there is high interest towards CNN-based detectors, and early results are already very promising. Recent studies in computer vision, however, have shown CNNs to be highly vulnerable to adversarial attacks, small perturbations of the input data which drive the network towards erroneous classification. In this paper we analyze the vulnerability of CNN-based image forensics methods to adversarial attacks, considering several detectors and several types of attack, and testing performance on a wide range of common manipulations, both easily and hardly detectable