The Information Processing and Algorithms Laboratory (iPAL) is directed by Prof. Vishal Monga. Graduate research in iPAL broadly encompasses signal and image processing theory and applications with a particular focus on capturing practical real-world constraints via convex optimization theory and algorithms.
This project proposed a scene information estimation network for dehazing for challenging benchmark image datasets of NTIRE'19 and NTIRE'18. The proposed networks `At-DH' and `AtJ-DH' can outperform state-of-the-art alternatives, especially when recovering images corrupted by dense hazes.
A DenseNet based dehazing network focusing on the recovery of the color information that comprises of: a common DenseNet based feature encoder whose output branches into three distinct DensetNet based decoders to yield estimates of the R, G and B color channels of the image.
Recognizing that a wavelet transform provides a “coarse” as well as “detail” separation of image content, we design a deep CNN to predict the “missing details” of wavelet coefficients of the low-resolution images to obtain the Super-Resolution (SR) results, which we name Deep Wavelet Super-Resolution (DWSR).
We propose a novel network structure for learning the SR mapping function in an image transformation domain, specifically discrete cosine transformation (DCT). The DCT is integrated into the network structure as a convolutional DCT (CDCT) layer which is trainable while maintaining its orthogonality properties with the orthogonality constraints.
We explore the use of image structures and physically meaningful priors in deep structures in order to achieve bet- ter performance.
Unlike regular optical imagery, for MR image super-resolution generous training is often unavailable. We therefore propose the use of image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image super-resolution.
We propose a Simultaneous Decomposition and Classification Network (SDCN) to eliminate noise interference, enhancing the classification accuracy.
Nuclei detection has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train for example convolutional neural networks (CNNs) with a training set of input images and known, labeled nuclei locations. Many of these methods are supplemented by spatial or morphological processing.
Vessel segmentation of retinal images is a key diagnostic capability in ophthalmology. This problem faces several challenges including low contrast, variable vessel size and thickness, and presence of interfering pathology such as micro-aneurysms and hemorrhages.
Fine-grained image classification has emerged as a significant challenge because objects in such images have small inter-class visual differences but with large variations in pose, lighting, and viewpoints, etc.