7 research outputs found
Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement
Burst image processing is becoming increasingly popular in recent years.
However, it is a challenging task since individual burst images undergo
multiple degradations and often have mutual misalignments resulting in ghosting
and zipper artifacts. Existing burst restoration methods usually do not
consider the mutual correlation and non-local contextual information among
burst frames, which tends to limit these approaches in challenging cases.
Another key challenge lies in the robust up-sampling of burst frames. The
existing up-sampling methods cannot effectively utilize the advantages of
single-stage and progressive up-sampling strategies with conventional and/or
recent up-samplers at the same time. To address these challenges, we propose a
novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a
spatially precise high-quality image from a burst of low-quality raw images.
GMTNet consists of three modules optimized for burst processing tasks:
Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment,
Transposed-Attention Feature Merging (TAFM) for multi-frame feature
aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale
merged features and construct a high-quality output image. Detailed
experimental analysis on five datasets validates our approach and sets a
state-of-the-art for burst super-resolution, burst denoising, and low-light
burst enhancement.Comment: Accepted at CVPR 202
Burstormer: Burst Image Restoration and Enhancement Transformer
On a shutter press, modern handheld cameras capture multiple images in rapid
succession and merge them to generate a single image. However, individual
frames in a burst are misaligned due to inevitable motions and contain multiple
degradations. The challenge is to properly align the successive image shots and
merge their complimentary information to achieve high-quality outputs. Towards
this direction, we propose Burstormer: a novel transformer-based architecture
for burst image restoration and enhancement. In comparison to existing works,
our approach exploits multi-scale local and non-local features to achieve
improved alignment and feature fusion. Our key idea is to enable inter-frame
communication in the burst neighborhoods for information aggregation and
progressive fusion while modeling the burst-wide context. However, the input
burst frames need to be properly aligned before fusing their information.
Therefore, we propose an enhanced deformable alignment module for aligning
burst features with regards to the reference frame. Unlike existing methods,
the proposed alignment module not only aligns burst features but also exchanges
feature information and maintains focused communication with the reference
frame through the proposed reference-based feature enrichment mechanism, which
facilitates handling complex motions. After multi-level alignment and
enrichment, we re-emphasize on inter-frame communication within burst using a
cyclic burst sampling module. Finally, the inter-frame information is
aggregated using the proposed burst feature fusion module followed by
progressive upsampling. Our Burstormer outperforms state-of-the-art methods on
burst super-resolution, burst denoising and burst low-light enhancement. Our
codes and pretrained models are available at https://
github.com/akshaydudhane16/BurstormerComment: Accepted at CVPR 202
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
NTIRE 2019 Image Dehazing Challenge Report
This paper reviews the second NTIRE challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The training data consists from 55 hazy images (with dense haze generated in an indoor or outdoor environment) and their corresponding ground truth (haze-free) images of the same scene. The dense haze has been produced using a professional haze/fog generator that imitates the real conditions of haze scenes. The evaluation consists from the comparison of the dehazed images with the ground truth images. The dehazing process was learnable through provided pairs of haze-free and hazy train images. There were 270 registered participants and 23 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing