Burst Image Restoration and Enhancement.

Burst Image Restoration aims to reconstruct a high-quality image by efficiently combining complementary inter-frame information. However, it is quite challenging since individual burst images often have inter-frame misalignments that usually lead to ghosting and zipper artifacts. To mitigate this, we develop a novel approach for burst image processing named BIPNet that focuses solely on the information exchange between burst frames and filter-out the inherent degradations while preserving and enhancing the actual scene details. Our central idea is to generate a set of pseudo-burst features that combine complementary information from all the burst frames to exchange information seamlessly. However, due to inter-frame misalignment, the information cannot be effectively combined in pseudo-burst. Thus, we initially align the incoming burst features regarding the reference frame using the proposed edge-boosting feature alignment. Lastly, we progressively upscale the pseudo-burst features in multiple stages while adaptively combining the complementary information. Unlike the existing works, that usually deploy single-stage up-sampling with a late fusion scheme, we first deploy a pseudo-burst mechanism followed by the adaptive-progressive feature up-sampling. The proposed BIPNet significantly outperforms the existing methods on burst super-resolution, low-light image enhancement, low-light image super-resolution, and denoising tasks. The pre-trained models and source code are available at https://github.com/akshaydudhane16/BIPNet.

An Unified Recurrent Video Object Segmentation Framework for Various Surveillance Environments.

Moving object segmentation (MOS) in videos received considerable attention because of its broad security-based applications like robotics, outdoor video surveillance, self-driving cars, etc. The current prevailing algorithms highly depend on additional trained modules for other applications or complicated training procedures or neglect the inter-frame spatio-temporal structural dependencies. To address these issues, a simple, robust, and effective unified recurrent edge aggregation approach is proposed for MOS, in which additional trained modules or fine-tuning on a test video frame(s) are not required. Here, a recurrent edge aggregation module (REAM) is proposed to extract effective foreground relevant features capturing spatio-temporal structural dependencies with encoder and respective decoder features connected recurrently from previous frame. These REAM features are then connected to a decoder through skip connections for comprehensive learning named as temporal information propagation. Further, the motion refinement block with multi-scale dense residual is proposed to combine the features from the optical flow encoder stream and the last REAM module for holistic feature learning. Finally, these holistic features and REAM features are given to the decoder block for segmentation. To guide the decoder block, previous frame output with respective scales is utilized. The different configurations of training-testing techniques are examined to evaluate the performance of the proposed method. Specifically, outdoor videos often suffer from constrained visibility due to different environmental conditions and other small particles in the air that scatter the light in the atmosphere. Thus, comprehensive result analysis is conducted on six benchmark video datasets with different surveillance environments. We demonstrate that the proposed method outperforms the state-of-the-art methods for MOS without any pre-trained module, fine-tuning on the test video frame(s) or complicated training.

RYF-Net: Deep Fusion Network for Single Image Haze Removal.

Haze removal from a single image is a challenging task. Estimation of accurate scene transmission map (TrMap) is the key to reconstruct the haze-free scene. In this paper, we propose a convolutional neural network based architecture to estimate the TrMap of the hazy scene. The proposed network takes the hazy image as an input and extracts the haze relevant features using proposed RNet and YNet through RGB and YCbCr color spaces respectively and generates two TrMaps. Further, we propose a novel TrMap fusion network (FNet) to integrate two TrMaPs and estimate robust TrMap for the hazy scene. To analyze the robustness of FNet, we tested it on combinations of TrMaps obtained from existing state-of-the-art methods. Performance evaluation of the proposed approach has been carried out using the structural similarity index, mean square error and peak signal to noise ratio. We conduct experiments on five datasets namely: D-HAZY ancuti2016d, Imagenet deng2009imagenet, Indoor SOTS li2017reside, HazeRD zhang2017hazerd and set of real-world hazy images. Performance analysis shows that the proposed approach outperforms the existing state-of-the-art methods for single image dehazing. Further, we extended our work to address high-level vision task such as object detection in hazy scenes. It is observed that there is a significant improvement in accurate object detection in hazy scenes using proposed approach.

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms.

Breast Cancer is the most prevalent cancer among women across the globe. Automatic detection of breast cancer using Computer Aided Diagnosis (CAD) system suffers from false positives (FPs). Thus, reduction of FP is one of the challenging tasks to improve the performance of the diagnosis systems. In the present work, new FP reduction technique has been proposed for breast cancer diagnosis. It is based on appropriate integration of preprocessing, Self-organizing map (SOM) clustering, region of interest (ROI) extraction, and FP reduction. In preprocessing, contrast enhancement of mammograms has been achieved using Local Entropy Maximization algorithm. The unsupervised SOM clusters an image into number of segments to identify the cancerous region and extracts tumor regions (i.e., ROIs). However, it also detects some FPs which affects the efficiency of the algorithm. Therefore, to reduce the FPs, the output of the SOM is given to the FP reduction step which is aimed to classify the extracted ROIs into normal and abnormal class. FP reduction consists of feature mining from the ROIs using proposed local sparse curvelet coefficients followed by classification using artificial neural network (ANN). The performance of proposed algorithm has been validated using the local datasets as TMCH (Tata Memorial Cancer Hospital) and publicly available MIAS (Suckling et al., 1994) and DDSM (Heath et al., 2000) database. The proposed technique results in reduction of FPs from 0.85 to 0.02 FP/image for MIAS, 4.81 to 0.16 FP/image for DDSM, and 2.32 to 0.05 FP/image for TMCH reflecting huge improvement in classification of mammograms.